Development of an Instrument to Assess Service Quality and Resource Adequacy in Midwifery Laboratories
DOI:
https://doi.org/10.22487/nbm9ep81Keywords:
Instrument development, Midwifery laboratory, Psychometric evaluation, Resource adequacy, Service qualityAbstract
Background: Midwifery laboratories are essential for developing clinical competencies, yet service quality and resource adequacy are frequently assessed separately. Objectives: This study aimed to develop and internally evaluate an integrated instrument for assessing midwifery laboratory quality. Methods: Five experts validated the instrument, and 446 midwifery students with laboratory experience completed a 44 item questionnaire. Psychometric evaluation included content validity, exploratory factor analysis (EFA), preliminary confirmatory factor analysis (CFA), Rasch analysis, and internal consistency testing. Results: The average scale content validity index was 0.982. EFA identified six factors explaining 56.96% of total variance, with the first factor contributing 44.02%. Preliminary CFA demonstrated X²/df = 2.240, RMSEA = 0.053, SRMR = 0.041, GFI = 0.991, CFI = 0.898, and TLI = 0.886. Rasch person reliability was 0.969, whereas item reliability was 0.641. Cronbach alpha reached 0.970, with corrected item total correlations ranging from 0.561 to 0.739. Conclusions: The instrument demonstrated promising internal psychometric properties for evaluating midwifery laboratory quality. However, independent external validation and comprehensive ordinal and Rasch diagnostics remain necessary before broader implementation.
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1. Sweet L, Fleet J, Bull A, Downer T, Fox D, Bowman R, et al. Development and validation of the Australian Midwifery Standards Assessment Tool to the Australian Midwife Standards for Practice 2018. Women Birth. 2020;33(2):135-144. doi:10.1016/j.wombi.2019.08.004.
2. Sweet L, Bazargan M, McKellar L, Gray J, Henderson A. Validation of the Australian Midwifery Standards Assessment Tool: a tool to assess midwifery competence. Women Birth. 2018;31(1):59-68. doi:10.1016/j.wombi.2017.06.017.
3. Carter AG, Creedy DK, Sidebotham M. Critical thinking skills in midwifery practice: development of a self assessment tool for students. Midwifery. 2017;50:184-192. doi:10.1016/j.midw.2017.04.010.
4. Kolivand M, Esfandyari M, Heydarpour S. Examining validity and reliability of objective structured clinical examination for evaluation of clinical skills of midwifery undergraduate students: a descriptive study. BMC Med Educ. 2020;20:96. doi:10.1186/s12909-020-02017-4.
5. Cooper S, Cant R, Porter J, Bogossian F, McKenna L, Brady S, Fox Young S. Simulation based learning in midwifery education: a systematic review. Women Birth. 2012;25(2):64-78. doi:10.1016/j.wombi.2011.03.004.
6. Ben Yahya L, Naciri A, Radid M, Chemsi G. Immersive simulation in nursing and midwifery education: a systematic review. J Educ Eval Health Prof. 2024;21:19. doi:10.3352/jeehp.2024.21.19.
7. Flott EA, Linden L. The clinical learning environment in nursing education: a concept analysis. J Adv Nurs. 2016;72(3):501-513. doi:10.1111/jan.12861.
8. Nordquist J, Hall J, Caverzagie K, Snell L, Chan MK, Thoma B, et al. The clinical learning environment. Med Teach. 2019;41(4):366-372. doi:10.1080/0142159X.2019.1566601.
9. Parasuraman A, Zeithaml VA, Berry LL. SERVQUAL: a multiple item scale for measuring consumer perceptions of service quality. J Retailing. 1988;64(1):12-40.
10. Mosadeghrad AM. Factors influencing healthcare service quality. Int J Health Policy Manag. 2014;3(2):77-89. doi:10.15171/ijhpm.2014.65.
11. Huwae M, Raden JS, Pramesti LA, Julita FR, Alkhansa SN, Anwar AVN, et al. Analysis of inpatient care service quality in hospitals based on national standards: a literature review. Healthy Tadulako J. 2025;11(1):1-8. doi:10.22487/htj.v11i1.1217.
12. Sirenden I, Ilyas M, Chandra F, Zamli. Analysis of the quality of health services based on the implementation of accreditation on patient satisfaction at Puskesmas Teminabuan, South Sorong Regency. Healthy Tadulako J. 2025;11(1):56-62. doi:10.22487/htj.v11i1.1509.
13. Sim JJM, Rusli KDB, Seah B, Levett Jones T, Lau Y, Liaw SY. Virtual simulation to enhance clinical reasoning in nursing: a systematic review and meta analysis. Clin Simul Nurs. 2022;69:26-39. doi:10.1016/j.ecns.2022.05.006.
14. Hegland PA, Aarlie H, Strømme H, Jamtvedt G. Simulation based training for nurses: systematic review and meta analysis. Nurse Educ Today. 2017;54:6-20. doi:10.1016/j.nedt.2017.04.004.
15. Cant RP, Cooper SJ. Use of simulation based learning in undergraduate nurse education: an umbrella systematic review. Nurse Educ Today. 2017;49:63-71. doi:10.1016/j.nedt.2016.11.015.
16. Kim J, Park JH, Shin S. Effectiveness of simulation based nursing education depending on fidelity: a meta analysis. BMC Med Educ. 2016;16:152. doi:10.1186/s12909-016-0672-7.
17. Watts PI, Rossler K, Bowler F, Miller C, Charnetski M, Decker S, et al. Onward and upward: introducing the Healthcare Simulation Standards of Best Practice. Clin Simul Nurs. 2021;58:1-4. doi:10.1016/j.ecns.2021.08.006.
18. Boateng GO, Neilands TB, Frongillo EA, Melgar Quiñonez HR, Young SL. Best practices for developing and validating scales for health, social, and behavioral research: a primer. Front Public Health. 2018;6:149. doi:10.3389/fpubh.2018.00149.
19. Terwee CB, Prinsen CAC, Chiarotto A, Westerman MJ, Patrick DL, Alonso J, et al. COSMIN methodology for evaluating the content validity of patient reported outcome measures: a Delphi study. Qual Life Res. 2018;27(5):1159-1170. doi:10.1007/s11136-018-1829-0.
20. Polit DF, Beck CT, Owen SV. Is the CVI an acceptable indicator of content validity? Appraisal and recommendations. Res Nurs Health. 2007;30(4):459-467. doi:10.1002/nur.20199.
21. Mokkink LB, Terwee CB, Patrick DL, Alonso J, Stratford PW, Knol DL, Bouter LM, de Vet HCW. The COSMIN checklist for assessing methodological quality of studies on measurement properties of health status measurement instruments: an international Delphi study. Qual Life Res. 2010;19(4):539-549. doi:10.1007/s11136-010-9606-8.
22. Prinsen CAC, Mokkink LB, Bouter LM, Alonso J, Patrick DL, de Vet HCW, Terwee CB. COSMIN guideline for systematic reviews of patient reported outcome measures. Qual Life Res. 2018;27(5):1147-1157. doi:10.1007/s11136-018-1798-3.
23. Chooha U, Chotwattanakulchai N, Sirisome J. Measurement of midwifery clinical leadership among nursing students: exploratory factor analysis. Adv Med Educ Pract. 2022;13:1329-1339. doi:10.2147/AMEP.S375854.
24. Tavakol M, Wetzel A. Factor analysis: a means for theory and instrument development in support of construct validity. Int J Med Educ. 2020;11:245-247. doi:10.5116/ijme.5f96.0f4a.
25. Watkins MW. Exploratory factor analysis: a guide to best practice. J Black Psychol. 2018;44(3):219-246. doi:10.1177/0095798418771807.
26. Goretzko D, Pham TTH, Bühner M. Exploratory factor analysis: current use, methodological developments and recommendations for good practice. Curr Psychol. 2021;40(7):3510-3521. doi:10.1007/s12144-019-00300-2.
27. Hu LT, Bentler PM. Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Struct Equ Modeling. 1999;6(1):1-55. doi:10.1080/10705519909540118.
28. Marsh HW, Hau KT, Wen Z. In search of golden rules: comment on hypothesis testing approaches to setting cutoff values for fit indexes and dangers in overgeneralizing Hu and Bentler findings. Struct Equ Modeling. 2004;11(3):320-341. doi:10.1207/s15328007sem1103_2.
29. Schreiber JB, Nora A, Stage FK, Barlow EA, King J. Reporting structural equation modeling and confirmatory factor analysis results: a review. J Educ Res. 2006;99(6):323-338. doi:10.3200/JOER.99.6.323-338.
30. Tennant A, Conaghan PG. The Rasch measurement model in rheumatology: what is it and why use it? When should it be applied, and what should one look for in a Rasch paper? Arthritis Rheum. 2007;57(8):1358-1362. doi:10.1002/art.23108.
31. Planinic M, Boone WJ, Susac A, Ivanjek L. Rasch analysis in physics education research: why measurement matters. Phys Rev Phys Educ Res. 2019;15(2):020111. doi:10.1103/PhysRevPhysEducRes.15.020111.
32. Arifuddin H, Arismunandar A, Arifuddin A, Nur AF, Arifuddin H, Rahman A. The many facet Rasch model in the analysis of instruments measuring teachers' attitudes towards earthquake disaster preparedness in Indonesia. Healthy Tadulako J. 2024;10(1):156-172. doi:10.22487/htj.v10i1.1266.
33. Tavakol M, Dennick R. Making sense of Cronbach alpha. Int J Med Educ. 2011;2:53-55. doi:10.5116/ijme.4dfb.8dfd.
34. McNeish D. Thanks coefficient alpha, we will take it from here. Psychol Methods. 2018;23(3):412-433. doi:10.1037/met0000144.
35. Flora DB. Your coefficient alpha is probably wrong, but which coefficient omega is right? A tutorial on using R to obtain better reliability estimates. Adv Methods Pract Psychol Sci. 2020;3(4):484-501. doi:10.1177/2515245920951747.
36. McTague K, Smith V. Simulation based education for nurse and midwife advanced practitioner education: a scoping review protocol. JBI Evid Synth. 2023;21(7):1453-1460. doi:10.11124/JBIES-22-00111.
37. Mokkink LB, Boers M, van der Vleuten CPM, Bouter LM, Alonso J, Patrick DL, de Vet HCW, Terwee CB. COSMIN Risk of Bias tool to assess the quality of studies on reliability or measurement error of outcome measurement instruments: a Delphi study. BMC Med Res Methodol. 2020;20:293. doi:10.1186/s12874-020-01179-5.
38. Astuti AAP, Afianto R, Renaldi R, Muthmainnah, Sulistyowati M, Nurhayati A. Evaluation of the Islamic Boarding School Health Post Program (Poskestren) based on the CIPP (Context, Input, Process, Product) theory: a scoping review. Healthy Tadulako J. 2026;12(1):112-120. doi:10.22487/htj.v12i1.1940.
39. Adelia D, Indarwati F, Rahmah R, Suminar IT. The effect of educational interventions through animated video and booklet on parents' knowledge regarding cognitive stimulation in preschool aged children. Healthy Tadulako J. 2026;12(3):444-454. doi:10.22487/atvb7n86.
40. Azizah NPN, Adhi MHP, Suparti S, Fatwati NF. Comparison of self directed video and simulation methods on hand only CPR skill. Healthy Tadulako J. 2025;11(2):265-274. doi:10.22487/htj.v11i2.1630.
41. Lahay N, Suarayasa K, Rahman A. Analysis of the availability of healthcare human resources at health centers in Central Sulawesi Province for 2024. Healthy Tadulako J. 2026;12(3):403-411. doi:10.22487/gb027r92.
42. Pebrianton H, Komaruddin K, Ilmi MI. The influence of hospital management information system utilization and facilities on health worker performance at Rumkitalmar Ewa Pangalila in supporting marine soldier combat readiness. Healthy Tadulako J. 2026;12(2):342-352. doi:10.22487/w21z2j60.
43. Subekti AT, Atmoko D, Miftah M. The effectiveness of video database based education in improving occupational safety and health literacy among construction sector MSME workers. Healthy Tadulako J. 2026;12(2):307-315. doi:10.22487/dthpsz98.
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Copyright (c) 2026 Hidayanti Arifuddin, A Fahira Nur, Nur Dwi Yulianti, Devi Devinda Novitasari, Charisma Destrikasari, Wulan Anjani, Putri Yuniartis, Mita Herlina Sari, Siti Rahmadani Nasution, Adhar Arifuddin
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