ORIGINAL ARTICLE
Construct validity and reliability of the Chinese version of the Disaster Preparedness Evaluation Tool in Taiwan Tzu-Fei Chen, Kuei-Ru Chou, Yuan-Mei Liao, Cheng-Hsun Ho and Min-Huey Chung
Aims and objectives. To develop a Chinese version of the Disaster Preparedness Evaluation Tool and validate its psychometric properties. Background. An adequate disaster preparation programme for nurses has not been developed in Taiwan. To develop an exhaustive and effective educational programme on disaster preparation for nurses, a multidimensional instrument is required for assessing the disaster preparation level of nurses. Design. A cross-sectional study was conducted. Methods. In total, 1550 of 2226 public health nurses in 15 counties completed the self-administrated questionnaire. We randomly selected 805 samples to examine the factor structure and factor model by using exploratory factor analysis and confirmatory factor analysis. The convergent validity was measured using the average variance extracted and composite reliability. Results. Five factors, namely postdisaster management, skills, knowledge of selfpreparation in a disaster, knowledge to respond in the community, and knowledge to respond in the workplace, were extracted, and explained 6513% of the total variance. An acceptable model fit was identified using confirmatory factor analysis. The Cronbach’s a coefficient of the Chinese version of the Disaster Preparedness Evaluation Tool was 097. Significant values of the average variance extracted greater than 05 indicated convergent validity. Conclusion. The Chinese version of the Disaster Preparedness Evaluation Tool is a reliable and valid instrument for measuring disaster preparation. Relevance to clinical practice. The Chinese version of the Disaster Preparedness Evaluation Tool provides reliable and valid measures that can be used to evaluate the disaster preparedness of nurses. The items in the instrument can be used to identify the dimension of disaster management in all stages, and can form the essential foundation of an education and training programme for public health nurses to reduce the harm of disasters and promote community resilience.
What does this paper contribute to the wider global clinical community?
• Developed Chinese version of the
•
•
Disaster Preparedness Evaluation Tool and the psychometric properties revealed acceptable and validity of the instrument. Providing a method to measure the Chinese-speaking public health nurses’ preparedness for disaster management. Further education and training programme can be formed for public health nurses in disaster.
Key words: confirmatory factor analysis, Disaster Preparedness Evaluation Tool, reliability, validity Accepted for publication: 10 September 2014 Authors: Tzu-Fei Chen, RN, MSN, Doctoral Student, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taipei and Lecturer, Department of Nursing, Min-Hwei College of Health Care Management, Tainan; Kuei-Ru Chou, PhD, RN, Professor, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taiwan; Yuan-Mei Liao, PhD, RN, Associate Professor, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taipei; Cheng-Hsun Ho, PhD, Assistant
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing, doi: 10.1111/jocn.12721
Professor, Graduate Institute of Information Management, National Taipei University, Taipei; Min-Huey Chung, PhD, RN, Associate Professor, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan Correspondence: Dr Min-Huey Chung, Associate Professor, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, No 250, Wu-Hsing Street, Taipei City110, Taiwan. Telephone: +886 2 27397086. E-mail: [email protected]
1
T-F Chen et al.
Introduction Climate change has become the largest weapon of mass destruction in the world. Disasters caused by climate extremes have generated severe effects on the global community, and represent a threat to human life and the health care system (Intergovernmental Panel on Climate Change 2013). The World Health Organization (WHO) reported that more than 26 billion people have been affected by disasters, including earthquakes, tsunamis, landslides, typhoons, floods and heat waves that resulted in severe damage in the previous decade (United Nation 2014). In addition, the World Bank (2013) estimated that the frequency of disasters, number of people affected by disasters, and damaged area will considerably increase in the future. The WHO, International Council of Nurses and International Nursing Coalition for Mass Casualty Education (INCMCE) have asserted that competency in disaster preparation is imperative to reducing the harm from disasters, and have urged all health care professionals to take action regarding disasters (Al Khalaileh et al. 2010, Ireland et al. 2006, INCMCE 2003). Therefore, it is needed to evaluate the current disaster preparedness for all filed nurses before establish the essential foundation of education and training programme in disaster management.
Background The ‘Hyogo Declaration’ and the ‘Hyogo Framework for Action 2005–2015—Building the Resilience for Nations and Communities for Disasters’ are regarded as the United Nations’ commitment to concrete actions for mitigating disasters (United Nations Office for Disaster Risk Reduction 2005). These documents have addressed disaster preparation in relation to disaster management, including prevention, mitigation, response and debriefing, as the main strategy for reducing disasters. Disaster preparation should focus on risk assessment, education and training, and constructing an early warning system. Regarding disaster response plans, these documents emphasise several key points: (1) developing and encouraging community participation, (2) collaboration and coordination in cross-institutions, (3) integrating volunteer resources and (4) clarifying the role of each professional. In particular, training programmes should be community based and oriented towards local volunteers to strengthen the capacity of mitigation and enable people to cope with disasters. A disaster is a dynamic long-term cumulative process, is usually unpredictable, and causes multiple types of damage.
2
Engaging in preparedness, response and evaluation is an effective approach to preventing crises generated by disasters (Fink 1986). The ability of health providers plays a vital role in assisting victims of disasters and enhancing community resilience (Dugan 2007, Taylor et al. 2010). Therefore, nurses in all fields must understand disaster management, including the knowledge and skills required to cope with various disaster situations, and must take action to detect problems early and satisfy the initial needs of victims. However, not all nurses, particularly community nurses, are adequately prepared for such challenges. Many studies have reported that nurses lack experience and do not have enough knowledge and skills in disaster management (Al Khalaileh et al. 2010, 2012, Fung et al. 2008, Nasrabadi et al. 2007, Ticky et al. 2009, Yang et al. 2010). Fung et al. (2008) reported that 97% of nurses (164 participants) are not adequately prepared for disasters. Moreover, Al Khalaileh et al. (2012) reported that 65% of the 474 participants in their study described themselves as poorly prepared for disasters. Currently, many institutions have established disaster training programmes for nurses, but most programmes are hospital oriented, and only a few programmes include content related to community resources (Chapman & Arbon 2008). Some studies have indicated that community care nurses play a vital role in the immediate response to disaster management, and collaboration with outside groups across all stages of disaster nursing is necessary (Jennings-Sanders & Frisch 2005, Yang et al. 2010). Therefore, an adequate training programme for community nurses must be developed. Core disaster competencies are developed through longterm educational programmes. In Western countries, numerous schools and hospitals provide training programmes and the educational programmes of some organisations have included disasters in their curriculums for nursing students and healthcare providers in all fields (Chapman & Arbon 2008). However, disaster preparation programmes remain underdeveloped in Taiwan. To initiate a comprehensive and effective educational programme on disaster preparation for all nurses in Taiwan, the disaster preparation level of nurses should be assessed in advance. The Disaster Preparedness Evaluation Tool (DPET) has been successfully used for evaluating disaster preparation in English and Arabic contexts, but not for evaluating the disaster preparedness of Chinese-speaking nurses; moreover, the construct validity of the DPET has not been tested. The purpose of this study was to develop a Chinese version of the DPET and to validate its psychometric properties.
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Original article
Aim The aim of this study was to test the psychometric properties of the Chinese version of the Disaster Preparedness Evaluation Tool (DPET-C).
Research method Instrument Bond and Tichy developed the original DPET in 2007 (Al Khalaileh et al. 2010, Ticky et al. 2009) to assess the preparedness of nurse practitioners for disasters. The DPET consists of 68 items; 25 items relate to predisaster preparedness, 16 items relate to mitigation, six items relate to the debriefing stage of a disaster response and 21 are openended questions regarding demographic data. Participants rate the first 47 items on a six-point Likert scale from 1–6, with a higher score indicating greater disaster preparedness. The DPET was determined to have adequate internal consistency; the Cronbach’s a coefficients were 091, 093, 093 and 091 for the overall scale, predisaster preparedness, mitigation and debriefing stage respectively (Al Khalaileh et al. 2010). Al Khalaileh et al. translated the DPET into Arabic, and used it to assess the preparedness of nurses for disaster management (Al Khalaileh et al. 2010, 2012). The Cronbach’s a coefficients for the overall reliability, knowledge subscale, skills subscale and postdisaster management subscale of the Arabic version of the DPET were 090, 090, 091 and 091, respectively, indicating that the internal consistency was as high as that of the original DPET.
Translation process An adaptation of Brislin’s translation model (Jones et al. 2001) was used to guide the translation of the DPET. First, the DPET was translated into traditional Chinese by two independent bilingual experts, and blindly backtranslated by two other bilingual experts. Subsequently, the bilingual experts met with members of the research group to review the backtranslation script to identify the most accurate and culturally equivalent meanings between English and traditional Chinese. Second, the new version of the DPET-C was independently backtranslated by two other bilingual experts who had not seen the original English version. A group discussion was conducted to compare the original and backtranslated versions of the instrument. The semantic equivalence of the instrument was ensured through the group discussion process. After the discussion, the final
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
DEPT and validity
backtranslated version was sent to the DPET developer through e-mail, and the DPET developer was required to determine whether the backtranslated tool was identical to the original tool. This process was repeated until the backtranslated items and the originals conveyed the same meanings. During this process, the original tool developer suggested that the researchers split Item 3 and Item 13 into two subitems to ensure that the items are semantically clear, and agreed to add a demographic question to enrich data analysis. Finally, the reliability and validity of the DPET-C tool were evaluated. To ensure cultural equivalence, including semantic, idiomatic, experiential and conceptual equivalence, between the English version and traditional Chinese version of the DPET, a panel of experts was invited to review all reports. According to Beaton et al. (2000), an expert committee should include health professionals, methodologists, language professionals and translators. Thus, seven experts were invited, namely two head nurses in public health, two methodologists, one emergency physician who specialised in disaster management, one language professor who has owned an international language certification and one community-health faculty member. The experts rated each item according to relevance and clarity of the content by using a content validity index (CVI). The following four-point Likert scale was used: 1 = not relevant, 2 = somewhat relevant, 3 = relevant and 4 = highly relevant. The CVI was computed by ‘the proportion of items on an instrument that achieved a rating of 3 or 4 by all the content experts (Polit & Beck 2006)’. In our study, the average of item-level content validity (I-CVI) for all items of the scale was 098. All of these results indicated that the DPET-C exhibited adequate content validity (Lynn 1986, Norwood 2000). The final version of the DPET-C was completed after the experts suggested a slight rewording to clarify meanings in traditional Chinese. A pilot study involving 39 public health nurses (PHNs) from the target population was conducted. Two weeks later, the questionnaire was distributed again. The intraclass correlation coefficient (ICC) was to assess the consistency of measurements among multiple observers (Shrout & Fleiss 1979). The results of the pilot study revealed high internal consistency reliability (Cronbach’s a = 097 for the overall scale). The ICC was 085 (p < 001) for the total scores, indicating acceptable stability over a two-week period.
Design A cross-sectional design was used in this study.
3
T-F Chen et al.
Participants The target of this study was PHNs in Taiwan. PHNs who worked full time and were able to read, write and speak Chinese were included, whereas nurse attendants and PHNs who worked part time were excluded. There are 373 public health centres or units in 22 counties in Taiwan, and 239 public health centres or units in 15 counties agreed to participate in this study. In total, 2226 questionnaires were distributed, and 1733 copies were returned. The return rate was 779%. After 21 unqualified responders and 162 invalid responses were subtracted, the remaining 1550 (696%) valid copies were used in the subsequent analyses. A randomisation technique using Bernoulli distribution was performed to split the data into two groups: Group 1 (n = 805) was the major data set in this study, and Group 2 (n = 745) was used as a cross-sample for performing a cross-validity analysis.
naires were returned to the researchers in sealed envelopes. The participants were voluntary and anonymous.
Data analysis
Data were collected from July 2012–January 2013. First, the research team gained permission from the health bureau of each county through administrative processes, and 15 of 22 counties approved this study. After obtaining permission, a researcher began calling the head nurse of each public health centre or unit to explain the purpose of the study. Subsequently, the DPET-C questionnaire and a stamped envelope were distributed to 239 public health centres or units that had provided informed consent, and we requested them to return the completed questionnaire before the deadline. After the participants received the questionnaires, they were mailed a gift coupon (50 NTD) as compensation for completing the survey. In addition, the researchers contacted the public health centres or units when (1) the questionnaire had not been received by the deadline or (2) the data were incomplete to increase the return rate and reduce the number of missing data.
SPSS for Windows version 17.0 and AMOS version 17.0 software (SPSS Inc., Chicago, IL, USA) were used for data analysis. Before the analysis, that accuracy of all data was examined and missing values were identified. One hundred and eighty-three invalid questionnaires were detected and eliminated. Descriptive analysis was used to describe the demographic data. The internal consistency of the DPET-C was determined by using Cronbach’s a coefficient. The composite reliability (CR) was examined. Exploratory factor analysis (EFA) was used to establish the construct validity of the tool by performing principal components analysis (PCA) with varimax rotation. The suitability of the factor analysis was evaluated using the Kaiser–Meyer–Olkin (KMO) measure, Bartlett’s test, a scree plot and the Kaiser eigenvalues-greater-than-one rule. Moreover, items were excluded if they met the following criteria: (1) item-total correlation 2, (3) absolute value of kurtosis >2, (4) factor loading 005), indicating an adequate cross-validity (Table 5).
Discussion In this study, we demonstrated that the DPET-C exhibits high reliability and construct validity by performing EFA and CFA (Nunnally & Bernstein 1994). The DPET-C exhibits excellent reliability, with Cronbach’s a coefficients of 097 for all items, 094 for Items 1–25 (related to predisaster preparedness), 096 for Items 26–41 (related to mitigation) and 094 for Items 42–47 (related to the debriefing stage of a disaster response). The results indicated that the DPET-C exhibits high internal consistency, and its reliability is as high as those of the original DPET and Arabic version of the DPET (Al Khalaileh et al. 2010, Ticky et al. 2009).
8
PDM
0·81 0·85
0·60
KNC
0·86 0·59 0·57
KNW
0·78 0·84 0·61
Item 19
0·71
Item 9
0·80
Item 8
0·74
Item 10
0·40
Item 3-1
0·58
Item 3-2
0·74
Item 2
0·35
Item 16
0·32
Item 13-1
0·61
Item 13-2
0·70
Item 14
0·38
Figure 1 The factor structure of Disaster Preparedness Evaluation Tool – Chinese version (DPET-C). Parameter estimates are standardized. Model fit index: v2 = 71795 (df = 161, p = 000); goodness-of-fit index = 090, adjusted goodness-of-fit index = 090, comparative fit index = 093, root-mean-square error of approximation = 005, normed fit index = 090, related fit index = 090. Numbers of items correspond to those in Table 4.
The DPET-C contains 71 items, including 49 items related to disaster preparedness and 22 items related to demographic data, three more than the original tool does. During the translation process, we observed that both Item 3 (‘I know who to contact in disaster situations’, or ‘I know the chain of command in my community’) and Item 13 (‘I have a list of contacts in the medical or health community in which I practice. I know referral contacts in case of a disaster situation (e.g., health department)’) contained two concepts. According to the principles of questionnaire design and development, questions should be simple and contain only one concept, and double-barrelled questions should be avoided (Giesen et al. 2012, Rattray & Jones 2007). Therefore, we split Item 3 and Item 13 into two subitems, and added questions based on Items 3-1, 3-2 and Item 13-1, 13-2 to increase the clarity and precision of these items. We also added one demographic question © 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Original article
DEPT and validity
Table 5 Cross-validity
CMIN
p
Normed fit index Delta-1
Model
DF
Assuming model Structural Structural weights Structural covariances Structural residuals Measurement residuals Assuming model Structural Structural covariances Structural residuals Measurement residuals Assuming model Structural Structural residuals Measurement residuals
weights to be correct (model) 4 152 082 0000 5 291 071 0000 10 737 069 0000 28 3783 010 0002 weights to be correct (model) 1 140 024 0000 6 586 044 0000 24 3632 005 0002 covariances to be correct (model) 5 446 049 0000 23 3492 005 0002
IFI Delta-2
Related fit index rho-1
TLI rho-2
0000 0000 0000 0002
0001 0001 0003 0006
0001 0001 0003 0006
0000 0000 0002
0000 0002 0005
0000 0002 0005
0000 0002
0001 0005
0001 0005
DF, degree of freedom; CMIN = Chi-square Minimum; IFI, Incremental fit index; TLI, Tucker-Lewis-Index.
for statistical purposes. The advantages of the DPET-C compared with the original DPET and Arabic version of the DPET are improved clarity and a concept-oriented semantic and item content.
2009). However, in the current study, the knowledge level was clearly divided into three dimensions and reflected selfpreparation, response in the community and response in the workplace.
Exploratory factor analysis
Confirmatory factor analysis
Five factors were extracted from the DPET-C by performing EFA on all 49 items in the questionnaire. Eight factors with eigenvalues >1 and factor loadings >05 were initially extracted and explained 676% of the total variance, but these factors exhibited a poor fit, because not all factors had a minimum of three items. Based on the suggestions for attaining a stable factor structure, each factor should have at least three items and should explain 5% of the variance, and the total variance explained by all factors should be at least 60% to prevent an offending estimate (Hair et al. 2010). After we deleted 11 items that did not meet the criteria, five factors were extracted and exhibited satisfactory results; each factor consisted of at least three items, and each item explained more than 5% of the variance in each factor. The total explained variance of the five factors was 6513%. Moreover, the five factors of the DPET-C exhibited adequate psychometric properties with favourable internal consistency estimates (Cronbach’s a coefficients 078–093). We observed that the first two factors (PDM and SK) exhibited structures similar to those in both the original DPET and Arabic version of the DPET (Table 2), but the third to fifth factors (KN-SP, KWC and KNW) were slightly different. These three knowledge dimensions shared the knowledge component with the original DPET and Arabic version of the DPET (Al Khalaileh et al. 2010, Ticky et al.
After performing EFA, we applied CFA to further test the construct validity of the DPET-C. The AVE and CR results for the five factors indicated that the DPET-C exhibits high reliability and convergent validity (Table 4). The intercorrelation among the five factors indicated that the discriminant validity was high, because the square root of the AVE was greater than 05 for each factor (Table 3). To test the model structure, the suitability of the data set was established according to several fit indices, namely AGFI, GFI, CFI, NFI, RFI and RMSEA. Hu and Bentler (1999) asserted that the RMSEA is the most sensitive index for evaluating models and suggested that the RMSEA value be under 006. In the current study, the RMSEA value was 005, confirming that the final result of the 18 items of the DPET-C exhibited an adequate model structure for the five factors. All items were perfectly reflected on the latent factors, and all factors exhibited significant factor loadings between 053–095 towards the latent construct of the second-order DPET-C. Furthermore, we split the sample into two parts to assess the cross-sample validity and, thus, verify the stability of the model structure. The model comparison revealed an equal fit in the two subsamples (p > 005, TFI ≦ 005), and validity generalisation of the DPET-C was performed. Based on the results of the CFA, 13 items were removed from the first factor (PDM). This factor contained five items,
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
9
T-F Chen et al.
which reflected how a PHN treats patients after a disaster occurs: ‘I am familiar with psychological interventions, behavioural therapy, cognitive strategies, support groups, and incident debriefing for patients who experience emotional or physical trauma,’ ‘I feel reasonably confident that I can treat patients independently, without supervision of a physician in a disaster situation,’ ‘I would feel confident implementing emergency plans, evacuation procedures, and similar functions,’ ‘I would feel confident providing education to patients regarding stress and abnormal functioning related to trauma,’ and ‘I am familiar with what the scope of my role as a registered nurse in a postdisaster situation would be.’ This result is not surprising because most PHNs in Taiwan usually work independently, without physician supervision, and are capable of educating people in the community. By contrast, this result also revealed a lack of confidence among PHNs in managing patients with PTSD and becoming the first responder in a disaster situation. The second factor (SK) was focused on bioterrorism or biological attacks; it contained three items: ‘In case of bioterrorism or a biological attack, I know how to use personal protective equipment,’ ‘In case of bioterrorism or a biological attack, I know how to execute decontamination procedures,’ and ‘In case of bioterrorism or a biological attack, I know how to perform isolation procedures so that I can minimise the risks of community exposure’. This result revealed the most pressing needs regarding the disaster skills of Taiwanese PHNs. Taiwan experiences many natural disasters, but biological attacks are rare. Nevertheless, since the catastrophic events of 9/11 in the USA, PHNs in Taiwan have become increasingly aware of this threat. The third factor (KN-SP) reflected the characteristic of disaster preparedness as follows: ‘I find that the research literature on disaster preparedness and management is easily accessible,’ ‘I find that the research literature on disaster preparedness is understandable,’ and ‘I consider myself prepared for managing disasters.’ The results revealed that the PHNs are consciously well prepared for disasters. The fourth factor (KWC) and the fifth factor (KNW) both retained the same items, and each item had a factor loading greater than 05. In the present study, the final Chinese version of DPET is contained 38 items through EFA. Five factors were identified and strongly supported the concept of disaster preparedness. The questionnaire developed was also highly correlated with the original DPET and Arabic version of the DPET. Thus, the DPET-C exhibits high reliability and construct validity, with favourable convergent and discriminant validity. Moreover, the 18 items through CFA process
10
are more precisely reflect on each factor, and is noticeable shorter than DPET-C and original DPET. We adapted the higher level of criteria to test our instrument across every analysis step of CFA. That is, we believe it may be seemed as a short form of DPET-C to use in the clinical setting and quickly assess the disaster competency.
Conclusion Our results indicated that the DPET-C is a reliable, valid and sensitive instrument for evaluating disaster preparedness. The DPET-C contains five essential components related to disaster preparedness: PDM, skills and three knowledge dimensions (the predisaster, mitigation and debriefing stages). The DPET-C may thus be used to evaluate the disaster competency of PHNs in Taiwan.
Limitations This study had three limitations. First, the data were obtained using self-report methods; thus, bias may have occurred. Second, most PHNs were female (992%), which limit generalisation of the results. Third, we split the sample from the original data set into two groups to evaluate the cross-validity. Ideally, two separate populations should be used to strengthen the validity of the scale. Further research considering a diverse sample of nurses is thus warranted.
Relevance to clinical practice The DPET-C provides reliable and valid measures that can be used to evaluate the preparedness of nurses in disaster preparedness. The items of the DPET-C provide a disaster management dimension at all stages; thus, the DPET-C can form the essential foundation of an education and training programme for PHNs to reduce the harm of disasters.
Acknowledgements The authors thank Professor Renea Beckstrand from Brigham Young University in the USA for authorising the translation of the DPET, assisting with editing and making suggestions throughout the translation process.
Disclosure The authors have confirmed that all authors meet the ICMJE criteria for authorship credit (www.icmje.org/ ethical_1author.html), as follows: (1) substantial contribu© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Original article
DEPT and validity
tions to conception and design of, or acquisition of data or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content and (3) final approval of the version to be published.
Conflict of interest None.
References Al Khalaileh MA, Bond AE, Beckstrand RL & Al-Talafha A (2010) The disaster preparedness evaluation tool: psychometric testing of the classical Arabic version. Journal of Advanced Nursing 66, 664–672. Al Khalaileh MA, Bond E & Alasad JA (2012) Jordanian nurses’ perceptions of their preparedness for disaster management. International Emergency Nursing 20, 14–23. Beaton DE, Bombardier C, Guillemin F & Ferraz MB (2000) Guideline for the process of cross-cultural adaptation of self-report measures. Spine 25, 3186– 3191. Bentler PM (1990) Comparative fit indices in structure models. Psychological Bulletin 107, 238–246. Chapman K & Arbon P (2008) Are nurses ready? Disaster preparedness in the acute setting. Australian Emergency Nursing Journal 11, 135–144. Dugan B (2007) Loss of identity in disaster: how do you say goodbye to home? Perspective in Psychiatric Care 43, 41–46. Fink S (1986) Crisis Management: Planning for the Inevitable. American Management Association, New York, NY. Fornell C & Larcker DF (1981) Evaluating structure equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 39–50. Fung OWM, Loke AY & Lai CKY (2008) Disaster preparedness among Hong Kong nurses. Journal of Advanced Nursing 62, 698–703. Giesen D, Meertens V, Vis-Visschers R & Beukenhorst D (2012) Questionnaire Development. The Hague, Heerlen, Netherlands. Hair JF, Black WC, Babin BJ & Anderson RE (2010) Multivariate Data Analysis: A Global Perspective, 7th edn. Pearson Education International, Prentice Hall.
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Hu L & Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6, 1–55. Intergovernmental Panel on Climate Change (2013) Climate Change 2013: The Physical Science Basis. Available at: http://www.ipcc.ch (accessed 12 February 2014). International Nursing Coalition for Mass Casualty (2003) Educational Competencies for Registered Nurses Responding to Mass Casualty Incidents. Available at: http://www.nursing. vanderbilt.edu/incmce/modules.html (accessed 18 May 2013). Ireland M, Ea E, Kontzamanis E & Michel C (2006) Integrating disaster preparedness into a community health nursing course: one school’s experience. Disaster Management and Response 4, 72–76. Jennings-Sanders A & Frisch NWS (2005) Nursing students’ perceptions about disaster nursing. Disaster Management and Response 3, 80–85. Jones PS, Lee JW, Phillips LR, Zhang XE & Jaceldo KB (2001) An adaptation of Brislin’s translation model for crosscultural research. Nursing Research 50, 300–304. Lynn MR (1986) Determination and quantification of content validity. Nursing Research 35, 382–385. Nasrabadi AN, Naji H, Mirzabeigi G & Dadbakhs M (2007) Earthquake relief: Iranian nurses’ responses in Bam, 2003, and lessons learned. International Nursing Review 54, 13–18. Norwood SL (2000) Research Strategies for Advanced Practices Nurses. PrenticeHall Health, Upper Saddle River, NJ. Nunnally JC & Bernstein IH (1994) Psychometric Theory, 3rd edn. McGrawHill, New York. Polit DF & Beck CT (2006) The content validity index: are you sure you know
what’s being reported? Critique and Recommendations. Research in Nursing & Health 29, 489–497. Rattray J & Jones MC (2007) Essential elements of questionnaire design and development. Journal of Clinical Nursing 16, 234–243. Shrout PE & Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psyhological Bulletin 86, 420–428. Tabachnick BG & Fidell LS (2007) Using Multivariate Statistics. Allyn & Bacon, Boston. Taylor M, Barr M, Stevens G, BrysonTaylor D, Agho K, Jacobs J & Raphael B (2010). Psychosocial stress and strategies for managing adversity: measuring population resilience in New South Wales, Australia. Population Health Metrics 8. Available at: http://www.pophealthmetrics. com/content/8/1/28 (accessed 4 April 2011). Ticky M, Bond AE, Beckstrand RL & Heise B (2009) Nurse practitioners’ perception of disaster preparedness education. The American Journal for Nurse Practitioners 13, 10–22. United Nation (2014) Overview of Global Humanitarian Report 2014. Available at: http://www.who.int/hac/en (accessed 1 March 2014). United Nations Office for Disaster Risk Reduction (2005) Hyogo Framework for Action. Available at: http://www. unisdr.org/we/coordinate/hfa (accessed 2 September 2013). World Bank (2013) Disaster Risk Management Overview. Available at: http:// www.worldbank.org/en/topic/disaster riskmanagement/overview (accessed 1 March 2014). Yang YN, Xiao LD, Cheng HY, Zhu JC & Arbon P (2010) Chinese nurses’ experience in the Wenchuan earthquake relief. International Nursing Review 57, 217–223.
11
T-F Chen et al.
The Journal of Clinical Nursing (JCN) is an international, peer reviewed journal that aims to promote a high standard of clinically related scholarship which supports the practice and discipline of nursing. For further information and full author guidelines, please visit JCN on the Wiley Online Library website: http:// wileyonlinelibrary.com/journal/jocn
Reasons to submit your paper to JCN: High-impact forum: one of the world’s most cited nursing journals, with an impact factor of 1316 – ranked 21/101 (Nursing (Social Science)) and 25/103 Nursing (Science) in the 2012 Journal Citation Reportsâ (Thomson Reuters, 2012). One of the most read nursing journals in the world: over 19 million full text accesses in 2011 and accessible in over 8000 libraries worldwide (including over 3500 in developing countries with free or low cost access). Early View: fully citable online publication ahead of inclusion in an issue. Fast and easy online submission: online submission at http://mc.manuscriptcentral.com/jcnur. Positive publishing experience: rapid double-blind peer review with constructive feedback. Online Open: the option to make your article freely and openly accessible to non-subscribers upon publication in Wiley Online Library, as well as the option to deposit the article in your preferred archive.
12
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Construct validity and reliability of the Chinese version of the Disaster Preparedness Evaluation Tool in Taiwan Tzu-Fei Chen, Kuei-Ru Chou, Yuan-Mei Liao, Cheng-Hsun Ho and Min-Huey Chung
Aims and objectives. To develop a Chinese version of the Disaster Preparedness Evaluation Tool and validate its psychometric properties. Background. An adequate disaster preparation programme for nurses has not been developed in Taiwan. To develop an exhaustive and effective educational programme on disaster preparation for nurses, a multidimensional instrument is required for assessing the disaster preparation level of nurses. Design. A cross-sectional study was conducted. Methods. In total, 1550 of 2226 public health nurses in 15 counties completed the self-administrated questionnaire. We randomly selected 805 samples to examine the factor structure and factor model by using exploratory factor analysis and confirmatory factor analysis. The convergent validity was measured using the average variance extracted and composite reliability. Results. Five factors, namely postdisaster management, skills, knowledge of selfpreparation in a disaster, knowledge to respond in the community, and knowledge to respond in the workplace, were extracted, and explained 6513% of the total variance. An acceptable model fit was identified using confirmatory factor analysis. The Cronbach’s a coefficient of the Chinese version of the Disaster Preparedness Evaluation Tool was 097. Significant values of the average variance extracted greater than 05 indicated convergent validity. Conclusion. The Chinese version of the Disaster Preparedness Evaluation Tool is a reliable and valid instrument for measuring disaster preparation. Relevance to clinical practice. The Chinese version of the Disaster Preparedness Evaluation Tool provides reliable and valid measures that can be used to evaluate the disaster preparedness of nurses. The items in the instrument can be used to identify the dimension of disaster management in all stages, and can form the essential foundation of an education and training programme for public health nurses to reduce the harm of disasters and promote community resilience.
What does this paper contribute to the wider global clinical community?
• Developed Chinese version of the
•
•
Disaster Preparedness Evaluation Tool and the psychometric properties revealed acceptable and validity of the instrument. Providing a method to measure the Chinese-speaking public health nurses’ preparedness for disaster management. Further education and training programme can be formed for public health nurses in disaster.
Key words: confirmatory factor analysis, Disaster Preparedness Evaluation Tool, reliability, validity Accepted for publication: 10 September 2014 Authors: Tzu-Fei Chen, RN, MSN, Doctoral Student, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taipei and Lecturer, Department of Nursing, Min-Hwei College of Health Care Management, Tainan; Kuei-Ru Chou, PhD, RN, Professor, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taiwan; Yuan-Mei Liao, PhD, RN, Associate Professor, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taipei; Cheng-Hsun Ho, PhD, Assistant
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing, doi: 10.1111/jocn.12721
Professor, Graduate Institute of Information Management, National Taipei University, Taipei; Min-Huey Chung, PhD, RN, Associate Professor, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan Correspondence: Dr Min-Huey Chung, Associate Professor, Graduate Institute of Nursing, College of Nursing, Taipei Medical University, No 250, Wu-Hsing Street, Taipei City110, Taiwan. Telephone: +886 2 27397086. E-mail: [email protected]
1
T-F Chen et al.
Introduction Climate change has become the largest weapon of mass destruction in the world. Disasters caused by climate extremes have generated severe effects on the global community, and represent a threat to human life and the health care system (Intergovernmental Panel on Climate Change 2013). The World Health Organization (WHO) reported that more than 26 billion people have been affected by disasters, including earthquakes, tsunamis, landslides, typhoons, floods and heat waves that resulted in severe damage in the previous decade (United Nation 2014). In addition, the World Bank (2013) estimated that the frequency of disasters, number of people affected by disasters, and damaged area will considerably increase in the future. The WHO, International Council of Nurses and International Nursing Coalition for Mass Casualty Education (INCMCE) have asserted that competency in disaster preparation is imperative to reducing the harm from disasters, and have urged all health care professionals to take action regarding disasters (Al Khalaileh et al. 2010, Ireland et al. 2006, INCMCE 2003). Therefore, it is needed to evaluate the current disaster preparedness for all filed nurses before establish the essential foundation of education and training programme in disaster management.
Background The ‘Hyogo Declaration’ and the ‘Hyogo Framework for Action 2005–2015—Building the Resilience for Nations and Communities for Disasters’ are regarded as the United Nations’ commitment to concrete actions for mitigating disasters (United Nations Office for Disaster Risk Reduction 2005). These documents have addressed disaster preparation in relation to disaster management, including prevention, mitigation, response and debriefing, as the main strategy for reducing disasters. Disaster preparation should focus on risk assessment, education and training, and constructing an early warning system. Regarding disaster response plans, these documents emphasise several key points: (1) developing and encouraging community participation, (2) collaboration and coordination in cross-institutions, (3) integrating volunteer resources and (4) clarifying the role of each professional. In particular, training programmes should be community based and oriented towards local volunteers to strengthen the capacity of mitigation and enable people to cope with disasters. A disaster is a dynamic long-term cumulative process, is usually unpredictable, and causes multiple types of damage.
2
Engaging in preparedness, response and evaluation is an effective approach to preventing crises generated by disasters (Fink 1986). The ability of health providers plays a vital role in assisting victims of disasters and enhancing community resilience (Dugan 2007, Taylor et al. 2010). Therefore, nurses in all fields must understand disaster management, including the knowledge and skills required to cope with various disaster situations, and must take action to detect problems early and satisfy the initial needs of victims. However, not all nurses, particularly community nurses, are adequately prepared for such challenges. Many studies have reported that nurses lack experience and do not have enough knowledge and skills in disaster management (Al Khalaileh et al. 2010, 2012, Fung et al. 2008, Nasrabadi et al. 2007, Ticky et al. 2009, Yang et al. 2010). Fung et al. (2008) reported that 97% of nurses (164 participants) are not adequately prepared for disasters. Moreover, Al Khalaileh et al. (2012) reported that 65% of the 474 participants in their study described themselves as poorly prepared for disasters. Currently, many institutions have established disaster training programmes for nurses, but most programmes are hospital oriented, and only a few programmes include content related to community resources (Chapman & Arbon 2008). Some studies have indicated that community care nurses play a vital role in the immediate response to disaster management, and collaboration with outside groups across all stages of disaster nursing is necessary (Jennings-Sanders & Frisch 2005, Yang et al. 2010). Therefore, an adequate training programme for community nurses must be developed. Core disaster competencies are developed through longterm educational programmes. In Western countries, numerous schools and hospitals provide training programmes and the educational programmes of some organisations have included disasters in their curriculums for nursing students and healthcare providers in all fields (Chapman & Arbon 2008). However, disaster preparation programmes remain underdeveloped in Taiwan. To initiate a comprehensive and effective educational programme on disaster preparation for all nurses in Taiwan, the disaster preparation level of nurses should be assessed in advance. The Disaster Preparedness Evaluation Tool (DPET) has been successfully used for evaluating disaster preparation in English and Arabic contexts, but not for evaluating the disaster preparedness of Chinese-speaking nurses; moreover, the construct validity of the DPET has not been tested. The purpose of this study was to develop a Chinese version of the DPET and to validate its psychometric properties.
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Original article
Aim The aim of this study was to test the psychometric properties of the Chinese version of the Disaster Preparedness Evaluation Tool (DPET-C).
Research method Instrument Bond and Tichy developed the original DPET in 2007 (Al Khalaileh et al. 2010, Ticky et al. 2009) to assess the preparedness of nurse practitioners for disasters. The DPET consists of 68 items; 25 items relate to predisaster preparedness, 16 items relate to mitigation, six items relate to the debriefing stage of a disaster response and 21 are openended questions regarding demographic data. Participants rate the first 47 items on a six-point Likert scale from 1–6, with a higher score indicating greater disaster preparedness. The DPET was determined to have adequate internal consistency; the Cronbach’s a coefficients were 091, 093, 093 and 091 for the overall scale, predisaster preparedness, mitigation and debriefing stage respectively (Al Khalaileh et al. 2010). Al Khalaileh et al. translated the DPET into Arabic, and used it to assess the preparedness of nurses for disaster management (Al Khalaileh et al. 2010, 2012). The Cronbach’s a coefficients for the overall reliability, knowledge subscale, skills subscale and postdisaster management subscale of the Arabic version of the DPET were 090, 090, 091 and 091, respectively, indicating that the internal consistency was as high as that of the original DPET.
Translation process An adaptation of Brislin’s translation model (Jones et al. 2001) was used to guide the translation of the DPET. First, the DPET was translated into traditional Chinese by two independent bilingual experts, and blindly backtranslated by two other bilingual experts. Subsequently, the bilingual experts met with members of the research group to review the backtranslation script to identify the most accurate and culturally equivalent meanings between English and traditional Chinese. Second, the new version of the DPET-C was independently backtranslated by two other bilingual experts who had not seen the original English version. A group discussion was conducted to compare the original and backtranslated versions of the instrument. The semantic equivalence of the instrument was ensured through the group discussion process. After the discussion, the final
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
DEPT and validity
backtranslated version was sent to the DPET developer through e-mail, and the DPET developer was required to determine whether the backtranslated tool was identical to the original tool. This process was repeated until the backtranslated items and the originals conveyed the same meanings. During this process, the original tool developer suggested that the researchers split Item 3 and Item 13 into two subitems to ensure that the items are semantically clear, and agreed to add a demographic question to enrich data analysis. Finally, the reliability and validity of the DPET-C tool were evaluated. To ensure cultural equivalence, including semantic, idiomatic, experiential and conceptual equivalence, between the English version and traditional Chinese version of the DPET, a panel of experts was invited to review all reports. According to Beaton et al. (2000), an expert committee should include health professionals, methodologists, language professionals and translators. Thus, seven experts were invited, namely two head nurses in public health, two methodologists, one emergency physician who specialised in disaster management, one language professor who has owned an international language certification and one community-health faculty member. The experts rated each item according to relevance and clarity of the content by using a content validity index (CVI). The following four-point Likert scale was used: 1 = not relevant, 2 = somewhat relevant, 3 = relevant and 4 = highly relevant. The CVI was computed by ‘the proportion of items on an instrument that achieved a rating of 3 or 4 by all the content experts (Polit & Beck 2006)’. In our study, the average of item-level content validity (I-CVI) for all items of the scale was 098. All of these results indicated that the DPET-C exhibited adequate content validity (Lynn 1986, Norwood 2000). The final version of the DPET-C was completed after the experts suggested a slight rewording to clarify meanings in traditional Chinese. A pilot study involving 39 public health nurses (PHNs) from the target population was conducted. Two weeks later, the questionnaire was distributed again. The intraclass correlation coefficient (ICC) was to assess the consistency of measurements among multiple observers (Shrout & Fleiss 1979). The results of the pilot study revealed high internal consistency reliability (Cronbach’s a = 097 for the overall scale). The ICC was 085 (p < 001) for the total scores, indicating acceptable stability over a two-week period.
Design A cross-sectional design was used in this study.
3
T-F Chen et al.
Participants The target of this study was PHNs in Taiwan. PHNs who worked full time and were able to read, write and speak Chinese were included, whereas nurse attendants and PHNs who worked part time were excluded. There are 373 public health centres or units in 22 counties in Taiwan, and 239 public health centres or units in 15 counties agreed to participate in this study. In total, 2226 questionnaires were distributed, and 1733 copies were returned. The return rate was 779%. After 21 unqualified responders and 162 invalid responses were subtracted, the remaining 1550 (696%) valid copies were used in the subsequent analyses. A randomisation technique using Bernoulli distribution was performed to split the data into two groups: Group 1 (n = 805) was the major data set in this study, and Group 2 (n = 745) was used as a cross-sample for performing a cross-validity analysis.
naires were returned to the researchers in sealed envelopes. The participants were voluntary and anonymous.
Data analysis
Data were collected from July 2012–January 2013. First, the research team gained permission from the health bureau of each county through administrative processes, and 15 of 22 counties approved this study. After obtaining permission, a researcher began calling the head nurse of each public health centre or unit to explain the purpose of the study. Subsequently, the DPET-C questionnaire and a stamped envelope were distributed to 239 public health centres or units that had provided informed consent, and we requested them to return the completed questionnaire before the deadline. After the participants received the questionnaires, they were mailed a gift coupon (50 NTD) as compensation for completing the survey. In addition, the researchers contacted the public health centres or units when (1) the questionnaire had not been received by the deadline or (2) the data were incomplete to increase the return rate and reduce the number of missing data.
SPSS for Windows version 17.0 and AMOS version 17.0 software (SPSS Inc., Chicago, IL, USA) were used for data analysis. Before the analysis, that accuracy of all data was examined and missing values were identified. One hundred and eighty-three invalid questionnaires were detected and eliminated. Descriptive analysis was used to describe the demographic data. The internal consistency of the DPET-C was determined by using Cronbach’s a coefficient. The composite reliability (CR) was examined. Exploratory factor analysis (EFA) was used to establish the construct validity of the tool by performing principal components analysis (PCA) with varimax rotation. The suitability of the factor analysis was evaluated using the Kaiser–Meyer–Olkin (KMO) measure, Bartlett’s test, a scree plot and the Kaiser eigenvalues-greater-than-one rule. Moreover, items were excluded if they met the following criteria: (1) item-total correlation 2, (3) absolute value of kurtosis >2, (4) factor loading 005), indicating an adequate cross-validity (Table 5).
Discussion In this study, we demonstrated that the DPET-C exhibits high reliability and construct validity by performing EFA and CFA (Nunnally & Bernstein 1994). The DPET-C exhibits excellent reliability, with Cronbach’s a coefficients of 097 for all items, 094 for Items 1–25 (related to predisaster preparedness), 096 for Items 26–41 (related to mitigation) and 094 for Items 42–47 (related to the debriefing stage of a disaster response). The results indicated that the DPET-C exhibits high internal consistency, and its reliability is as high as those of the original DPET and Arabic version of the DPET (Al Khalaileh et al. 2010, Ticky et al. 2009).
8
PDM
0·81 0·85
0·60
KNC
0·86 0·59 0·57
KNW
0·78 0·84 0·61
Item 19
0·71
Item 9
0·80
Item 8
0·74
Item 10
0·40
Item 3-1
0·58
Item 3-2
0·74
Item 2
0·35
Item 16
0·32
Item 13-1
0·61
Item 13-2
0·70
Item 14
0·38
Figure 1 The factor structure of Disaster Preparedness Evaluation Tool – Chinese version (DPET-C). Parameter estimates are standardized. Model fit index: v2 = 71795 (df = 161, p = 000); goodness-of-fit index = 090, adjusted goodness-of-fit index = 090, comparative fit index = 093, root-mean-square error of approximation = 005, normed fit index = 090, related fit index = 090. Numbers of items correspond to those in Table 4.
The DPET-C contains 71 items, including 49 items related to disaster preparedness and 22 items related to demographic data, three more than the original tool does. During the translation process, we observed that both Item 3 (‘I know who to contact in disaster situations’, or ‘I know the chain of command in my community’) and Item 13 (‘I have a list of contacts in the medical or health community in which I practice. I know referral contacts in case of a disaster situation (e.g., health department)’) contained two concepts. According to the principles of questionnaire design and development, questions should be simple and contain only one concept, and double-barrelled questions should be avoided (Giesen et al. 2012, Rattray & Jones 2007). Therefore, we split Item 3 and Item 13 into two subitems, and added questions based on Items 3-1, 3-2 and Item 13-1, 13-2 to increase the clarity and precision of these items. We also added one demographic question © 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Original article
DEPT and validity
Table 5 Cross-validity
CMIN
p
Normed fit index Delta-1
Model
DF
Assuming model Structural Structural weights Structural covariances Structural residuals Measurement residuals Assuming model Structural Structural covariances Structural residuals Measurement residuals Assuming model Structural Structural residuals Measurement residuals
weights to be correct (model) 4 152 082 0000 5 291 071 0000 10 737 069 0000 28 3783 010 0002 weights to be correct (model) 1 140 024 0000 6 586 044 0000 24 3632 005 0002 covariances to be correct (model) 5 446 049 0000 23 3492 005 0002
IFI Delta-2
Related fit index rho-1
TLI rho-2
0000 0000 0000 0002
0001 0001 0003 0006
0001 0001 0003 0006
0000 0000 0002
0000 0002 0005
0000 0002 0005
0000 0002
0001 0005
0001 0005
DF, degree of freedom; CMIN = Chi-square Minimum; IFI, Incremental fit index; TLI, Tucker-Lewis-Index.
for statistical purposes. The advantages of the DPET-C compared with the original DPET and Arabic version of the DPET are improved clarity and a concept-oriented semantic and item content.
2009). However, in the current study, the knowledge level was clearly divided into three dimensions and reflected selfpreparation, response in the community and response in the workplace.
Exploratory factor analysis
Confirmatory factor analysis
Five factors were extracted from the DPET-C by performing EFA on all 49 items in the questionnaire. Eight factors with eigenvalues >1 and factor loadings >05 were initially extracted and explained 676% of the total variance, but these factors exhibited a poor fit, because not all factors had a minimum of three items. Based on the suggestions for attaining a stable factor structure, each factor should have at least three items and should explain 5% of the variance, and the total variance explained by all factors should be at least 60% to prevent an offending estimate (Hair et al. 2010). After we deleted 11 items that did not meet the criteria, five factors were extracted and exhibited satisfactory results; each factor consisted of at least three items, and each item explained more than 5% of the variance in each factor. The total explained variance of the five factors was 6513%. Moreover, the five factors of the DPET-C exhibited adequate psychometric properties with favourable internal consistency estimates (Cronbach’s a coefficients 078–093). We observed that the first two factors (PDM and SK) exhibited structures similar to those in both the original DPET and Arabic version of the DPET (Table 2), but the third to fifth factors (KN-SP, KWC and KNW) were slightly different. These three knowledge dimensions shared the knowledge component with the original DPET and Arabic version of the DPET (Al Khalaileh et al. 2010, Ticky et al.
After performing EFA, we applied CFA to further test the construct validity of the DPET-C. The AVE and CR results for the five factors indicated that the DPET-C exhibits high reliability and convergent validity (Table 4). The intercorrelation among the five factors indicated that the discriminant validity was high, because the square root of the AVE was greater than 05 for each factor (Table 3). To test the model structure, the suitability of the data set was established according to several fit indices, namely AGFI, GFI, CFI, NFI, RFI and RMSEA. Hu and Bentler (1999) asserted that the RMSEA is the most sensitive index for evaluating models and suggested that the RMSEA value be under 006. In the current study, the RMSEA value was 005, confirming that the final result of the 18 items of the DPET-C exhibited an adequate model structure for the five factors. All items were perfectly reflected on the latent factors, and all factors exhibited significant factor loadings between 053–095 towards the latent construct of the second-order DPET-C. Furthermore, we split the sample into two parts to assess the cross-sample validity and, thus, verify the stability of the model structure. The model comparison revealed an equal fit in the two subsamples (p > 005, TFI ≦ 005), and validity generalisation of the DPET-C was performed. Based on the results of the CFA, 13 items were removed from the first factor (PDM). This factor contained five items,
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
9
T-F Chen et al.
which reflected how a PHN treats patients after a disaster occurs: ‘I am familiar with psychological interventions, behavioural therapy, cognitive strategies, support groups, and incident debriefing for patients who experience emotional or physical trauma,’ ‘I feel reasonably confident that I can treat patients independently, without supervision of a physician in a disaster situation,’ ‘I would feel confident implementing emergency plans, evacuation procedures, and similar functions,’ ‘I would feel confident providing education to patients regarding stress and abnormal functioning related to trauma,’ and ‘I am familiar with what the scope of my role as a registered nurse in a postdisaster situation would be.’ This result is not surprising because most PHNs in Taiwan usually work independently, without physician supervision, and are capable of educating people in the community. By contrast, this result also revealed a lack of confidence among PHNs in managing patients with PTSD and becoming the first responder in a disaster situation. The second factor (SK) was focused on bioterrorism or biological attacks; it contained three items: ‘In case of bioterrorism or a biological attack, I know how to use personal protective equipment,’ ‘In case of bioterrorism or a biological attack, I know how to execute decontamination procedures,’ and ‘In case of bioterrorism or a biological attack, I know how to perform isolation procedures so that I can minimise the risks of community exposure’. This result revealed the most pressing needs regarding the disaster skills of Taiwanese PHNs. Taiwan experiences many natural disasters, but biological attacks are rare. Nevertheless, since the catastrophic events of 9/11 in the USA, PHNs in Taiwan have become increasingly aware of this threat. The third factor (KN-SP) reflected the characteristic of disaster preparedness as follows: ‘I find that the research literature on disaster preparedness and management is easily accessible,’ ‘I find that the research literature on disaster preparedness is understandable,’ and ‘I consider myself prepared for managing disasters.’ The results revealed that the PHNs are consciously well prepared for disasters. The fourth factor (KWC) and the fifth factor (KNW) both retained the same items, and each item had a factor loading greater than 05. In the present study, the final Chinese version of DPET is contained 38 items through EFA. Five factors were identified and strongly supported the concept of disaster preparedness. The questionnaire developed was also highly correlated with the original DPET and Arabic version of the DPET. Thus, the DPET-C exhibits high reliability and construct validity, with favourable convergent and discriminant validity. Moreover, the 18 items through CFA process
10
are more precisely reflect on each factor, and is noticeable shorter than DPET-C and original DPET. We adapted the higher level of criteria to test our instrument across every analysis step of CFA. That is, we believe it may be seemed as a short form of DPET-C to use in the clinical setting and quickly assess the disaster competency.
Conclusion Our results indicated that the DPET-C is a reliable, valid and sensitive instrument for evaluating disaster preparedness. The DPET-C contains five essential components related to disaster preparedness: PDM, skills and three knowledge dimensions (the predisaster, mitigation and debriefing stages). The DPET-C may thus be used to evaluate the disaster competency of PHNs in Taiwan.
Limitations This study had three limitations. First, the data were obtained using self-report methods; thus, bias may have occurred. Second, most PHNs were female (992%), which limit generalisation of the results. Third, we split the sample from the original data set into two groups to evaluate the cross-validity. Ideally, two separate populations should be used to strengthen the validity of the scale. Further research considering a diverse sample of nurses is thus warranted.
Relevance to clinical practice The DPET-C provides reliable and valid measures that can be used to evaluate the preparedness of nurses in disaster preparedness. The items of the DPET-C provide a disaster management dimension at all stages; thus, the DPET-C can form the essential foundation of an education and training programme for PHNs to reduce the harm of disasters.
Acknowledgements The authors thank Professor Renea Beckstrand from Brigham Young University in the USA for authorising the translation of the DPET, assisting with editing and making suggestions throughout the translation process.
Disclosure The authors have confirmed that all authors meet the ICMJE criteria for authorship credit (www.icmje.org/ ethical_1author.html), as follows: (1) substantial contribu© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Original article
DEPT and validity
tions to conception and design of, or acquisition of data or analysis and interpretation of data, (2) drafting the article or revising it critically for important intellectual content and (3) final approval of the version to be published.
Conflict of interest None.
References Al Khalaileh MA, Bond AE, Beckstrand RL & Al-Talafha A (2010) The disaster preparedness evaluation tool: psychometric testing of the classical Arabic version. Journal of Advanced Nursing 66, 664–672. Al Khalaileh MA, Bond E & Alasad JA (2012) Jordanian nurses’ perceptions of their preparedness for disaster management. International Emergency Nursing 20, 14–23. Beaton DE, Bombardier C, Guillemin F & Ferraz MB (2000) Guideline for the process of cross-cultural adaptation of self-report measures. Spine 25, 3186– 3191. Bentler PM (1990) Comparative fit indices in structure models. Psychological Bulletin 107, 238–246. Chapman K & Arbon P (2008) Are nurses ready? Disaster preparedness in the acute setting. Australian Emergency Nursing Journal 11, 135–144. Dugan B (2007) Loss of identity in disaster: how do you say goodbye to home? Perspective in Psychiatric Care 43, 41–46. Fink S (1986) Crisis Management: Planning for the Inevitable. American Management Association, New York, NY. Fornell C & Larcker DF (1981) Evaluating structure equation models with unobservable variables and measurement error. Journal of Marketing Research 18, 39–50. Fung OWM, Loke AY & Lai CKY (2008) Disaster preparedness among Hong Kong nurses. Journal of Advanced Nursing 62, 698–703. Giesen D, Meertens V, Vis-Visschers R & Beukenhorst D (2012) Questionnaire Development. The Hague, Heerlen, Netherlands. Hair JF, Black WC, Babin BJ & Anderson RE (2010) Multivariate Data Analysis: A Global Perspective, 7th edn. Pearson Education International, Prentice Hall.
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
Hu L & Bentler PM (1999) Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling 6, 1–55. Intergovernmental Panel on Climate Change (2013) Climate Change 2013: The Physical Science Basis. Available at: http://www.ipcc.ch (accessed 12 February 2014). International Nursing Coalition for Mass Casualty (2003) Educational Competencies for Registered Nurses Responding to Mass Casualty Incidents. Available at: http://www.nursing. vanderbilt.edu/incmce/modules.html (accessed 18 May 2013). Ireland M, Ea E, Kontzamanis E & Michel C (2006) Integrating disaster preparedness into a community health nursing course: one school’s experience. Disaster Management and Response 4, 72–76. Jennings-Sanders A & Frisch NWS (2005) Nursing students’ perceptions about disaster nursing. Disaster Management and Response 3, 80–85. Jones PS, Lee JW, Phillips LR, Zhang XE & Jaceldo KB (2001) An adaptation of Brislin’s translation model for crosscultural research. Nursing Research 50, 300–304. Lynn MR (1986) Determination and quantification of content validity. Nursing Research 35, 382–385. Nasrabadi AN, Naji H, Mirzabeigi G & Dadbakhs M (2007) Earthquake relief: Iranian nurses’ responses in Bam, 2003, and lessons learned. International Nursing Review 54, 13–18. Norwood SL (2000) Research Strategies for Advanced Practices Nurses. PrenticeHall Health, Upper Saddle River, NJ. Nunnally JC & Bernstein IH (1994) Psychometric Theory, 3rd edn. McGrawHill, New York. Polit DF & Beck CT (2006) The content validity index: are you sure you know
what’s being reported? Critique and Recommendations. Research in Nursing & Health 29, 489–497. Rattray J & Jones MC (2007) Essential elements of questionnaire design and development. Journal of Clinical Nursing 16, 234–243. Shrout PE & Fleiss JL (1979) Intraclass correlations: uses in assessing rater reliability. Psyhological Bulletin 86, 420–428. Tabachnick BG & Fidell LS (2007) Using Multivariate Statistics. Allyn & Bacon, Boston. Taylor M, Barr M, Stevens G, BrysonTaylor D, Agho K, Jacobs J & Raphael B (2010). Psychosocial stress and strategies for managing adversity: measuring population resilience in New South Wales, Australia. Population Health Metrics 8. Available at: http://www.pophealthmetrics. com/content/8/1/28 (accessed 4 April 2011). Ticky M, Bond AE, Beckstrand RL & Heise B (2009) Nurse practitioners’ perception of disaster preparedness education. The American Journal for Nurse Practitioners 13, 10–22. United Nation (2014) Overview of Global Humanitarian Report 2014. Available at: http://www.who.int/hac/en (accessed 1 March 2014). United Nations Office for Disaster Risk Reduction (2005) Hyogo Framework for Action. Available at: http://www. unisdr.org/we/coordinate/hfa (accessed 2 September 2013). World Bank (2013) Disaster Risk Management Overview. Available at: http:// www.worldbank.org/en/topic/disaster riskmanagement/overview (accessed 1 March 2014). Yang YN, Xiao LD, Cheng HY, Zhu JC & Arbon P (2010) Chinese nurses’ experience in the Wenchuan earthquake relief. International Nursing Review 57, 217–223.
11
T-F Chen et al.
The Journal of Clinical Nursing (JCN) is an international, peer reviewed journal that aims to promote a high standard of clinically related scholarship which supports the practice and discipline of nursing. For further information and full author guidelines, please visit JCN on the Wiley Online Library website: http:// wileyonlinelibrary.com/journal/jocn
Reasons to submit your paper to JCN: High-impact forum: one of the world’s most cited nursing journals, with an impact factor of 1316 – ranked 21/101 (Nursing (Social Science)) and 25/103 Nursing (Science) in the 2012 Journal Citation Reportsâ (Thomson Reuters, 2012). One of the most read nursing journals in the world: over 19 million full text accesses in 2011 and accessible in over 8000 libraries worldwide (including over 3500 in developing countries with free or low cost access). Early View: fully citable online publication ahead of inclusion in an issue. Fast and easy online submission: online submission at http://mc.manuscriptcentral.com/jcnur. Positive publishing experience: rapid double-blind peer review with constructive feedback. Online Open: the option to make your article freely and openly accessible to non-subscribers upon publication in Wiley Online Library, as well as the option to deposit the article in your preferred archive.
12
© 2014 John Wiley & Sons Ltd Journal of Clinical Nursing
