Abstract
Background
Literature suggests that there is poor awareness and uptake of the Human Papillomavirus (HPV) vaccine in India. The role of cancer patients as potent advocates for HPV vaccine in their community is vital due to their first-hand experience with the turmoil that accompanies cancer. Hence, we have developed a study tool to measure the psychometric constructs “Awareness” of HPV vaccine among cancer patients and “Intention” to recommend the vaccine.
Methods
The theoretical concepts of the Health Belief Model were applied, feedback from oncologists at Basavatarakam Indo-American Cancer Hospital and Research Institute and public health experts at Indian Institute of Public Health to develop the study tool. A 24 items study tool was finalized following pre-testing and content validation. A sample size of 5:1 (participant:item) was considered adequate to conduct exploratory factor analysis (EFA). The pilot study tool was administered for validation to 150 cancer patients visiting the hospital during June–July 2023. The data were analysed using Jamovi (Version2.3).
Results
The McDonald’s omega was 0.8, which indicates good internal consistency of the data. EFA using parallel analysis with maximum likelihood extraction method and Promax oblique rotation with factor loadings above 0.4 revealed a 3-factor solution with 21 items. Factors were named “Capability”, “Awareness” and “Risk perception” respectively. The correlation between “Awareness” and “Risk Perception” was 0.28; between “Capability” and “Risk perception” 0.47. “Awareness” and “Capability” had a weak negative correlation(-0.02).
Conclusions
The study tool could effectively measure individual constructs of awareness and intention. Notably, our findings indicate a weak correlation between awareness and one component of intention (capability), within this population. This aspect, rigorously measured and validated by our study tool, holds significance as it implies that despite a low level of awareness in this population, they may still be considered as potentially influential advocates for the HPV vaccine.
Supplementary Information
The online version contains supplementary material available at 10.1007/s13224-024-01950-4.
Keywords: HPV vaccine, Awareness, Intention, Health belief model, Study tool, External factor analysis
Introduction
Cervical cancer is a major public health problem in India, predominantly affecting women of lower socioeconomic status [1]. Human papillomavirus (HPV) vaccines have proven to be effective for the prevention of HPV infection, the persistence of which may lead to cervical cancer [2]. In India, the HPV vaccines have been licensed for use since 2008 [3]. However, they have not yet been included in the Universal Immunization Program (UIP) [4]. The primary reasons for this are cost and vaccine hesitancy [5, 6]. With the development of CERVAVAC®, an affordable indigenous vaccine by the Serum Institute of India, the National Technical Advisory Group on Immunization (NTAGI) has recently considered its inclusion in the UIP [7]. However, there is poor awareness among the Indian population about the HPV infection and the role of HPV vaccine [5]. Since adolescent girls are the target population, parents and extended family members play an important role in vaccination-related decisions. Awareness about the HPV vaccine determines the intention to recommend the vaccine [8]. There are several studies which have explored HPV vaccine advocacy among cervical cancer survivors [9, 10]. However, research on the role of patients or survivors of any type of cancer with respect to HPV-vaccine-related advocacy in India is limited.
Since cancer patients are experienced in the turmoil that accompany cancer, they may serve as potent advocates of the HPV vaccine for their community. Hence, we have developed a study tool to measure the psychometric constructs “Awareness” about HPV vaccine among cancer patients and “Intention” to recommend HPV vaccine.
Objective
To develop and validate a study tool to measure awareness about HPV vaccine among cancer patients and their intention to vaccinate.
Method
The study was conducted in two phases:
Phase 1: Item Development
The Health Belief Model (HBM) was developed in the 1950’s by social psychologists to explain health behaviour and predict the uptake of health-related services [11]. The theoretical concepts of the HBM were applied to develop this study tool. Similar literature was reviewed and questions were modified to conform to the local context [12, 13]. A preliminary scale of 35 items was developed to measure two latent constructs: “Awareness” about HPV vaccine among cancer patients and “Intention” to recommend the vaccine to their family/ community members. “Awareness” is defined as an understanding that something exists, while “Intention” is defined as a prior conscious decision to perform a behaviour [14], 15]. The responses were recorded on a 5-point Likert scale (Strongly agree to strongly disagree). All responses were towards the strongly agree side except for the items 14, 17 and 18 (perceived barriers), the responses of which were towards the strongly disagree side. Therefore, these items were reverse coded during analysis. The preliminary scale of 35 items was sent for expert review to medical oncologists at Basavatarakam Indo-American Cancer Hospital and Research Institute (BIACHRI) and public health experts at Indian Institute of Public Health, Hyderabad (IIPHH) to check the ease of comprehension, relevance, and effectiveness of the questionnaire in capturing information relevant to the objectives of the study. The extent to which the questions were interpreted and understood correctly by the potential responders was assessed by the principal investigator in the field.
Translation of questionnaire: The initial translation from the English to Telugu was done by two independent translators, a language expert and a native speaker. Only one of these translators was aware of the objectives of the study [16, 17]. The Telugu version was back translated to English by two other independent translators, a language expert and a native speaker. None of the back translators were aware of the concepts of the questionnaire [17].
Phase 2: Scale Development
A sample size of 5–10 samples per item was considered adequate [18]. The study tool was administered to 150 cancer patients visiting the hospital during June–July 2023. Patients were approached at random in the Out Patient Departments (OPDs) of BIACHRI. Patients who were illiterate or spoke any language other than English/Telugu were excluded. The objective of the study was explained to the patients, and whomsoever consented to participate were included. The study tool was administered via patient-administered, paper-based mode.
Data Analysis
The data were entered in Microsoft excel and analysed using Jamovi (Version 2.3) [19]. The content validity was measured through the responses of 3 experts. (Content validity requires a minimum of 3 experts.) [20] They were requested to rate the items on a 4-point Likert scale: Not relevant = 1, Somewhat relevant = 2, Quite relevant = 3, Highly relevant = 4. The content validity index (I-CVI) was calculated in Excel as the proportion of participants who responded with “Quite relevant” or “Highly relevant”. Content validity index of the entire scale (S-CVI) was calculated as the average of I-CVI. The items with I-CVI < 0.8 were dropped [20]. The overall scale was considered valid if the S-CVI > 0.8 [20].
Since our scale consisted of multidimensional concepts, we preferred McDonald’s omega (ω) as it is a better measure of internal reliability compared to the popular Cronbach’s alpha [18]. McDonald’s omega of 0.8 or more was considered as good internal consistency [21].
An assumptions check was performed to determine if the data were fit for exploratory factor analysis (EFA). The Bartlett’s test for sphericity was considered significant if p < 0.05. The Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy > 0.7 was considered adequate [22]. Factor loadings above 0.4 were considered stable [23]. Parallel analysis is most accurate in determining the number of factors to be retained [24]. Promax oblique rotation is preferable in psychometric measures [25].
Results
The mean age of the study participants was 49 ± 13.6 years (SI Table I: Demographic characteristics). The majority of participants were female (74%). The education status was poor with 82% of the participants being illiterate or having received only high school education. Most of the participants were native from Telangana (66.6%), while 28% were from the neighbouring state, Andhra Pradesh. 51% of the participants were housewives, while 19% were farmers. Of the study participants, 42 (28%) had breast cancer, 31 (20.6%) oral cancer, 5 (3.33%) cervical cancer, 34 (22.6) gastrointestinal cancer, 28 (18.6%) other types of cancer. Ten (6.6%) participants did not mention their type of cancer.
The study tool consisted of a single questionnaire with two constructs: awareness and intention. The questionnaire could be filled in approximately 10–15 min. Following the expert review of the preliminary pool of 35 items, some items were rephrased and the order of the items was rearranged. The item “I believe that the adolescents in my family have a high chance of getting an HPV infection.” was removed, as it was not considered appropriate by the experts.
Following the content validity of the 34-item scale, the items with I-CVI < 0.8 were dropped. These were: “I have heard about vaccines”, “I know why HPV vaccine is given.”, “Cervical cancer is life threatening.”, “Cervical cancer treatment can have an impact on the entire family.”, “Vaccines are effective to prevent diseases.”, “I do not think the HPV vaccine is available at my native place.”, “I have to take off work to get my adolescent child vaccinated.”, “I will vaccinate my adolescent if the elders in my family agree.” and “I will vaccinate my adolescent if only one dose is sufficient for protection.” The overall scale was considered valid as the S-CVI was > 0.8 [20]. The study tool development and validation process is summarized in Fig. 1. The McDonald’s omega was 0.8, which indicates good internal consistency of the data (Table 1: Scale Reliability Statistics) [26]. An ω of 0.8 is associated with 20% error variance in a scale. The item total statistics is given in SI Table 2: Item Reliability Statistics, and explains how each item accounts for internal consistency. It denotes the correlation between that item and the sum of rest of the items. Item rest correlation of > 0.3 is considered adequate [27]. Deleting the items with correlation coefficient <0.3 (items 1,7,9, and 18) did not cause significant change in ω. Therefore, these items were retained.
Fig. 1.
Summary of study tool development and validation
Table 1.
Scale reliability statistics
| Mean | SD | McDonald's ω | |
|---|---|---|---|
| Scale | 3.32 | 0.527 | 0.803 |
Items 'rev_14: I believe the HPV vaccine is too expensive.', 'rev_17: I feel I have to travel too far to avail the vaccine.', and 'rev_18: I feel it will be difficult for me to take my adolescent child to get vaccinated multiple times.' correlate negatively with the total scale
Table 2.
Exploratory factor analysis: factor loadings
| Factor | Uniqueness | |||
|---|---|---|---|---|
| 1 | 2 | 3 | ||
| q20: I will vaccinate my adolescent if it is recommended by my doctor | 0.805 | 0.4635 | ||
| q21: I will vaccinate my adolescent if my family members agree | 0.780 | 0.4083 | ||
| q19: I will vaccinate my adolescent if I have more information about the vaccine | 0.745 | 0.3928 | ||
| q23: I believe that I am capable of convincing my adolescent child to be vaccinated | 0.744 | 0.3683 | ||
| q22: I believe I can motivate my friends and co-workers to learn about this vaccine | 0.718 | 0.4785 | ||
| q24: I believe that I am capable of advising my community members and relatives about this vaccine | 0.480 | 0.4947 | ||
| q8: I feel it is of benefit to prevent cervical cancer | 0.436 | 0.8232 | ||
| q15: I will vaccinate my adolescent if the vaccine is affordable for me | 0.408 | 0.7322 | ||
| q6: I know when the HPV vaccine is given | 1.003 | 0.0872 | ||
| q5: I know to whom the HPV vaccine is given | 0.996 | 0.0888 | ||
| q9: I have heard about a vaccine which can prevent cervical cancer in women | 0.608 | 0.6196 | ||
| q2: I have heard about HPV vaccine | 0.581 | 0.5827 | ||
| q1: I have heard about a viral infection which causes cancer in women | 0.458 | 0.7521 | ||
| q3: I believe that HPV infection is harmful | 0.7346 | |||
| q10: I feel that the adolescent girls in my family will be safe from cervical cancer if they are vaccinated with HPV vaccine | 0.665 | 0.5624 | ||
| q11: Cervical cancer treatment can lead to substantial financial loss | 0.567 | 0.6586 | ||
| rev_14: I believe the HPV vaccine is too expensive | − 0.554 | 0.6673 | ||
| rev_17: I feel I have to travel too far to avail the vaccine | − 0.520 | 0.7490 | ||
| rev_18: I feel it will be difficult for me to take my adolescent child to get vaccinated multiple times | − 0.499 | 0.8008 | ||
| q16: I believe the side effects of the vaccine are fewer compared to the consequences of cervical cancer | 0.494 | 0.6393 | ||
| q12: I believe that the HPV vaccine can prevent severe financial loss that may occur with cervical cancer treatment | 0.489 | 0.6024 | ||
| q4: I believe that the adolescents in my family have a high chance of getting an HPV infection | 0.433 | 0.7191 | ||
| q7: Cervical cancer is a severe disease | 0.8645 | |||
| q13: I believe it is more affordable to prevent rather than treat cancer | 0.7693 | |||
'Maximum likelihood' extraction method was used in combination with a 'promax' rotation
An EFA was run on a 24-item questionnaire (Appendix) that measured awareness of HPV vaccine and intention to recommend the vaccine, on sample size 150. The suitability of EFA was assessed prior to analysis. The overall KMO measure was 0.77 with individual KMO measures all greater than 0.7, classification of 'middling' according to Kaiser (1974) (SI Table III: KMO Measure of Sampling Adequacy). Bartlett's test of sphericity was statistically significant (p < 0.05), indicating that the data were likely factorizable (SI Table IV: Bartlett's Test of Sphericity).
Analyses of factor rotation were done using maximum likelihood extraction method with Promax oblique rotation (Table 2: Exploratory Factor Analysis: Factor Loadings). High factor loadings > 0.5 are highlighted in bold and indicate that these items are a useful measure of that particular factor and represent the correlation between variables within that particular factor. The last column of Table V describes the uniqueness or error related to each item, which is not explained by its respective factor. The higher the error, the lower the relevance of the contribution of the item in the factor [28]. The EFA revealed 3-Factor components that had eigenvalues greater than one. (SI Table V: Initial Eigenvalues). Visual inspection of the scree plot indicated that 3 factors should be retained (SI Fig I: Scree Plot). The cumulative variance explained by all 3 Factors was 41.4% (SI Table VI: Summary). The items under each Factor are given in (Table 3: Description of Factors). A total of 21 items were retained. Factor 1 was named “Capability”, Factor 2 “Awareness” and Factor 3 “Risk perception”. The correlation between “Awareness” and “Risk Perception” was 0.28; between “Capability” and “Risk perception” 0.47. “Awareness” and “Capability” had a weak negative correlation (-0.02) (SI Table VII: Inter-Factor Correlations).
Table 3.
Description of factors
| Item no | Factors | Categories |
|---|---|---|
| Factor 1: capability | ||
| 8 | I feel it is of benefit to prevent cervical cancer | Perceived benefit |
| 15 | I will vaccinate my adolescent if the vaccine is affordable for me | Cues to action |
| 19 | I will vaccinate my adolescent if I have more information about the vaccine | Cues to action |
| 20 | I will vaccinate my adolescent if it is recommended by my doctor | Cues to action |
| 21 | I will vaccinate my adolescent if my family members agree | Cues to action |
| 22 | I believe I can motivate my friends and co-workers to learn about this vaccine | Self-efficacy |
| 23 | I believe that I am capable of convincing my adolescent child to be vaccinated | Self-efficacy |
| 24 | I believe that I am capable of advising my community members and relatives about this vaccine | Self-efficacy |
| Factor 2: Awareness | ||
| 1 | I have heard about a viral infection which causes cancer in women | |
| 2 | I have heard about HPV vaccine | |
| 5 | I know to whom the HPV vaccine is given | |
| 6 | I know when the HPV vaccine is given | |
| 9 | I have heard about a vaccine which can prevent cervical cancer in women | |
| Factor 3: Risk Perceptions | ||
| 4 | I believe that the adolescents in my family have a high chance of getting an HPV infection | Perceived susceptibility |
| 10 | I feel that the adolescent girls in my family will be safe from cervical cancer if they are vaccinated with HPV vaccine | Perceived benefit |
| 11 | Cervical cancer treatment can lead to substantial financial loss | Perceived severity |
| 12 | I believe that the HPV vaccine can prevent severe financial loss that may occur with cervical cancer treatment | Perceived benefit |
| 14 | I believe the HPV vaccine is too expensive | Perceived barrier |
| 16 | I believe the side effects of the vaccine are fewer compared to the consequences of cervical cancer | Perceived benefit |
| 17 | I feel I have to travel too far to avail the vaccine | Perceived barrier |
| 18 | I feel it will be difficult for me to take my adolescent child to get vaccinated multiple times | Perceived barrier |
Discussion
The study aimed to develop and validate a tool exclusively for use among cancer patients, which follows the theoretical concepts of the HBM model and captures the latent constructs “Awareness of HPV vaccine” and “Intention to recommend”. To the best of our knowledge, a tool to measure the psychometric constructs of “awareness” of HPV vaccine among cancer patients and their “intention” to recommend the vaccine is not available. We consider this study population as being highly experienced in the turmoil that accompany cancer and therefore potential advocates of HPV vaccines in their community for the prevention of one particular cancer, i.e. cervical cancer. This consideration is backed by studies which show that cancer patients serve as promotors of cancer-related awareness [29, 30]. The sample size for the study was calculated based on the rule of thumb for calculating the sample size for EFA, i.e. 5:1 participants per item [27, 31]. We considered a sample size of 150 as adequate as is represented by the KMO classification as “middling”[22]. Other similar studies on behavioural science research have also considered such a sample size as adequate [32]. In fact, a study claims that a sample size as low as 50 may be adequate to measure psychometric properties of social construct [33]. This tool can be translated into any language. The appropriate process for validating a translated questionnaire needs to be followed before administration to the study population [16, 17].
There is a consensus in using Cronbach’s alpha as a measure of internal consistency [34]. However, we preferred to use McDonald’s omega rather than Cronbach’s alpha to measure the internal consistency of items across the scale. McDonald’s omega has emerged as a better measure of internal consistency in the presence of multidimensional concepts.[35] A scale is said to be multidimensional if it measures more than one latent variable or construct [36]. In our study, the latent variables measured were awareness and intention.
The item rest correlation represents how each item correlates with the rest of the scale and items with correlation less than 0.3 are to be omitted form the scale [37]. Items 1,7,9 and 18 show correlation less than 0.3; however, their omission does not lead to a significant reduction in omega. It is desirable to keep items which do not cause significant change in the internal consistency of the scale [38]. Therefore, these items were retained.
The three factors revealed by EFA have been named “Capability”, “Awareness” and “Risk perceptions”. Item 8 has loaded under “Capability” when in fact it is better positioned under “Risk perceptions”. This may probably be because the study population may have interpreted the statement “I feel it is of benefit to prevent cervical cancer” as considering themselves capable of taking the vaccine to prevent cervical cancer. The 3-Factor model may need further confirmation through confirmatory factor analysis (CFA). The communalities explained by the items varied from 20 to 70%. Items with high uniqueness and high correlation indicates that factor could be improved by including more items [39]. It has been suggested that items with communalities < 0.2 should be removed [23]. In our study, all 21 items had communalities > 0.2, and therefore, they were retained. Items 14,17 and 18 represent perceived barriers and show negative loadings, indicating that these items are related in the opposite direction to the factor “Risk perception”. The correlation between the domains “Capability” and “Risk perceptions” was 0.47. This is in line with the theoretical constructs of the HBM model, according to which risk perceptions guide the cues to action and determine self-efficacy [40]. The inter-factor correlation between awareness and one component of intention (capability) was weak in this population. This finding suggests that in this study population low awareness about HPV vaccine does not affect their capability to recommend the vaccine. This is an important aspect measured and validated by our study tool, as it suggests that despite poor awareness, this population may still be considered as potent advocates of the HPV vaccine. By developing this tool, we aim to demonstrate the potential for cancer patients to become advocates for HPV vaccination. The medical community can harness this information to further support the promotion of HPV vaccines through the voices of cancer survivors.
Conclusion
Our study tool could effectively measure individual constructs of “Awareness” and “Intention” with a 3-Factor model. It remains to be seen how the current model performs under CFA. We intend to conduct CFA at a later date.
Limitations
(1) The majority of the study population had poor education. We administered this self-reported questionnaire only to those participants who could read. Therefore, the applicability of this study tool for illiterate population could not be ascertained. Under such circumstances, a researcher guided study tool may be more appropriate. (2) For this self-reported questionnaire, 8 (5.3%) of the participant had not mentioned their education. (3) The cancer experience of patients was not measured in this study. It would be interesting to know how the experience of cancer patients correlates with their intention to recommend the HPV vaccine. (4) In this study, although the communality of items is acceptable (> 0.2) to retain them, it still indicates that individual item error is quite high.
Supplementary Information
Below is the link to the electronic supplementary material.
Acknowledgements
The authors would like to acknowledge the contribution of Ms Shivani Gujjula, junior assistant at BIACHRI for her assistance in data collection; and all the translators, namely Dr STP Lakshmi Prasanna, final year student of MPH at IIPHH, for her assistance in translation. In addition, we would also like to thank the clinical trial team at BIACHRI for their feedback in developing the study tool.
Funding
No funding was received for this study.
Declarations
Conflict of interest
The authors declare no conflicts of interest.
Ethics Approval
Ethics approval for the study was obtained from the institutional human ethics committee at Basavatarakam Indo-American Cancer Hospital and Research Institute (BIACHRI). Ethics approval was obtained from the (BIACHRI) ethics committee.
Footnotes
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
References
- 1.Agustiansyah P, Sanif R, Nurmaini S, Irfannuddin L. Epidemiology and risk factors for cervical cancer. Biosci Med J Biomed Transl Res. 2021;5:624–31.
- 2.Nour NM. Cervical cancer: a preventable death. Rev Obstet Gynecol. 2009;2:240–4. [PMC free article] [PubMed] [Google Scholar]
- 3.Sankaranarayanan R, Basu P, Kaur P, Bhaskar R, Singh GB, Denzongpa P, et al. Current status of human papillomavirus vaccination in India’s cervical cancer prevention efforts. Lancet Oncol. 2019 ;20:e637–44. Available from: https://pubmed.ncbi.nlm.nih.gov/31674322/ [DOI] [PubMed]
- 4.Bruni L, Saura-Lázaro A, Montoliu A, Brotons M, Alemany L, Diallo MS, et al. HPV vaccination introduction worldwide and WHO and UNICEF estimates of national HPV immunization coverage 2010–2019. Prev Med (Baltim). 2021;144: 106399. 10.1016/j.ypmed.2020.106399 [DOI] [PubMed] [Google Scholar]
- 5.Shah P, Shetty V, Ganesh M, Shetty AK. Challenges to human papillomavirus vaccine acceptability among women in south india: an exploratory study. Am J Trop Med Hyg. 2021;105:966–73. 10.4269/ajtmh.20-1650 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Roy S, Shankar A. HPV vaccination of girl child in india: intervention for primary prevention of cervical cancer. Asian Pac J Cancer Prev. 2018;19:2357. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Vaccine against cervical cancer to be included in national immunisation programme by mid-2023: NTAGI chief, ET HealthWorld. [cited 2023 Dec 22]. Available from: https://health.economictimes.indiatimes.com/news/policy/vaccine-against-cervical-cancer-to-be-included-in-national-immunisation-programme-by-mid-2023-ntagi-chief/96226416
- 8.Shah PM, Ngamasana E, Shetty V, Ganesh M, Shetty AK. Knowledge, attitudes and HPV vaccine intention among women in India. J Community Health. 2022;47:484–94. 10.1007/s10900-022-01072-w [DOI] [PubMed] [Google Scholar]
- 9.Ladd IG, Gogoi RP, Bogaczyk TL, Larson SL. Cervical cancer patients’ willingness and ability to serve as health care educators to advocate for human papillomavirus vaccine uptake. J Cancer Educ. 2019;34:608–13. 10.1007/s13187-018-1348-2 [DOI] [PubMed] [Google Scholar]
- 10.Shelal Z, Cho D, Urbauer DL, Lu Q, Ma BY, Rohrer AM, et al. Knowledge matters and empowers: HPV vaccine advocacy among HPV-related cancer survivors. Support Care Cancer. 2020;28:2407–13. 10.1007/s00520-019-05035-1 [DOI] [PubMed] [Google Scholar]
- 11.Coleman MT, Pasternak RH. Effective strategies for behavior change. Prim Care. 2012;39:281–305. 10.1016/j.pop.2012.03.004 [DOI] [PubMed] [Google Scholar]
- 12.Donadiki EM, Jiménez-García R, Hernández-Barrera V, Sourtzi P, Carrasco-Garrido P, López de Andrés A, et al. Health belief model applied to non-compliance with HPV vaccine among female university students. Public Health. 2014;128:268–73. [DOI] [PubMed]
- 13.McRee AL, Brewer NT, Reiter PL, Gottlieb SL, Smith JS. The Carolina HPV immunization attitudes and beliefs scale (CHIAS): scale development and associations with intentions to vaccinate. Sex Transm Dis. 2010;37:234–9. 10.1097/OLQ.0b013e3181c37e15 [DOI] [PubMed] [Google Scholar]
- 14.Awareness Definition & Meaning—Merriam-Webster [Internet]. [cited 2023 Aug 2]. Available from: https://www.merriam-webster.com/dictionary/awareness
- 15.APA Dictionary of Psychology. [cited 2023 Aug 2]. Available from: https://dictionary.apa.org/intention
- 16.(PDF) Recommendations for the Cross-Cultural Adaptation of the DASH & QuickDASH Outcome Measures Contributors to this Document. [cited 2023 Apr 6]. Available from: https://www.researchgate.net/publication/265000941_Recommendations_for_the_Cross-Cultural_Adaptation_of_the_DASH_QuickDASH_Outcome_Measures_Contributors_to_this_Document
- 17.Guillemin F, Bombardier C, Beaton D. Cross-cultural adaptation of health-related quality of life measures: literature review and proposed guidelines. J Clin Epidemiol. 1993;46:1417–32. 10.1016/0895-4356(93)90142-N [DOI] [PubMed] [Google Scholar]
- 18.F.DeVillis R. Scale development theory and applications. SAGE. 2017;3:10–27.
- 19.Jamovi—open statistical software for the desktop and cloud. [cited 2023 Jul 28]. Available from: https://www.jamovi.org/
- 20.Polit DF, Beck CT. The content validity index: are you sure you know what’s being reported? Critique and recommendations. Res Nurs Health. 2006;29:489–97. 10.1002/nur.20147 [DOI] [PubMed] [Google Scholar]
- 21.Feißt M, Hennigs A, Heil J, Moosbrugger H, Kelava A, Stolpner I, et al. Refining scores based on patient reported outcomes-statistical and medical perspectives. [cited 2023 Aug 2]; Available from: 10.1186/s12874-019-0806-9 [DOI] [PMC free article] [PubMed]
- 22.Shrestha N. Factor analysis as a tool for survey analysis. Am J Appl Math Stat. 2021;9:4–11. 10.12691/ajams-9-1-2 [DOI] [Google Scholar]
- 23.(PDF) Advice on exploratory factor analysis. [cited 2023 Aug 1]. Available from: https://www.researchgate.net/publication/319165677_Advice_on_Exploratory_Factor_Analysis
- 24.Hayton JC, Allen DG, Scarpello V. Factor retention decisions in exploratory factor analysis: a tutorial on parallel analysis. Organ Res Methods. 2004;7:191–205. 10.1177/1094428104263675 [DOI] [Google Scholar]
- 25.Hendrickson AE, White PO. PROMAX: a quick method for rotation to oblique simple structure. Br J Stat Psychol. 1964;17:65–70. 10.1111/j.2044-8317.1964.tb00244.x [DOI] [Google Scholar]
- 26.Feißt M, Hennigs A, Heil J, Moosbrugger H, Kelava A, Stolpner I, et al. Refining scores based on patient reported outcomes-statistical and medical perspectives. [DOI] [PMC free article] [PubMed]
- 27.Rattray J, Jones MC. Essential elements of questionnaire design and development. J Clin Nurs. 2007;16:234–43. 10.1111/j.1365-2702.2006.01573.x [DOI] [PubMed] [Google Scholar]
- 28.Foxcroft N. Learning Statistics With Jamovi Chapter 11,12,13,14. 2016;4:1–23.
- 29.Countries I of M (US) C on CC in LM-I, Sloan FA, Gelband H. Advocacy for cancer control 2007.
- 30.Rutledge R, Robinson L. Community-based organizations are critical partners in providing complete cancer care. Curr Oncol. 2009;16:29. 10.3747/co.v16i2.357 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Ali Memon M, Ting H, Cheah J-H, Thurasamy R, Chuah F, Huei CT. Sample size for survey research: review and recommendations. J Appl Struct Eqn Modell. 2020;4:2590–4221. [Google Scholar]
- 32.Mya KS, Zaw KK, Mya KM. Developing and validating a questionnaire to assess an individual’s perceived risk of four major non-communicable diseases in Myanmar. PLoS ONE. 2021;16:1–22. 10.1371/journal.pone.0234281 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 33.de Winter JCF, Dodou D, Wieringa PA. Exploratory factor analysis with small sample sizes. Multivariate Behav Res. 2009;44:147–81. 10.1080/00273170902794206 [DOI] [PubMed] [Google Scholar]
- 34.Taber KS. The use of Cronbach’s Alpha when developing and reporting research instruments in science education. Res Sci Educ. 2018;48:1273–96. 10.1007/s11165-016-9602-2 [DOI] [Google Scholar]
- 35.Watkins MW. The reliability of multidimensional neuropsychological measures: from alpha to omega. Clinical Neuropsychologist. 2017;31:1113–26. 10.1080/13854046.2017.1317364 [DOI] [PubMed] [Google Scholar]
- 36.Chapter 6 Measurement of Constructs | Research Methods for the Social Sciences. [cited 2023 Aug 2]. Available from: https://courses.lumenlearning.com/suny-hccc-research-methods/chapter/chapter-6-measurement-of-constructs/
- 37.Larsson F, Engström Å, Strömbäck U, Gustafsson S. Development and psychometric evaluation of the feeling safe during surgery scale. Nurs Open. 2021;8:2452–60. 10.1002/nop2.1003 [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Kumaranayake AR, Srimathi NL. Evaluation of internal consistency and factor structure of general health questionnaire (GHQ-28) on a South Indian sample. Int J Soc Sci Humanit Res. 2016;4:281–91. [Google Scholar]
- 39.Watkins MW. Exploratory factor analysis: a guide to best practice. J Black Psychol. 2018;44:219–46. 10.1177/0095798418771807 [DOI] [Google Scholar]
- 40.Paek H-J, Hove T. Risk perceptions and risk characteristics. Oxford Research Encyclopedia of Communication. 2017.
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