Constructing a Cognitive Map of Subject Domains in Community Medicine for Undergraduate Students: A Qualitative Study

Authors

  • Shuvajit Roy Assistant Professor, Community Medicine, JNM Medical College & Hospital, Nadia, West Bengal
  • Soumalya Ray Associate Professor, Community Medicine, Sarat Chandra Chattopadhyay Govt. Medical College, Howrah, West Bengal
  • Suman Das Assistant Professor, Community Medicine, Sarat Chandra Chattopadhyay Govt. Medical College, Howrah, West Bengal
  • Prasanta Ray Karmakar Professor and Head, Community Medicine, Sarat Chandra Chattopadhyay Govt. Medical College, Howrah, West Bengal

Keywords:

Cognitive Barrier, Curriculum, Medical Undergraduates, Public Health Education, Qualitative Research

Abstract

Background: The National Medical Commission (NMC) 2024 competency-based guidelines feature an extensive framework in Community Medicine. Navigating this vast curriculum imposes a massive cognitive load on undergraduate learners. This study aimed to map out student-perceived curriculum bottlenecks and explore the underlying reasons and respective remedial solutions. Methods: An institution-based qualitative study utilizing cultural domain analysis was conducted in May 2026 among 30 stratified purposively sampled Phase III MBBS students in West Bengal. Participants completed two independent tasks: a constrained ranking exercise of 20 NMC-mandated subject domains and an unconstrained pile sorting task. Data were analysed using multidimensional scaling (MDS), hierarchical cluster analysis (HCA), and manual thematic analysis. Results: Constrained ranking revealed "Biostatistics and its Applications" as the apex extreme bottleneck (Perceived Difficulty Score: 13.03). HCA and MDS partitioned the domains into four distinct thematic clusters. Cluster A (Core Epidemiology; Score: 12.00) carried analytical barriers and a high cognitive load. Cluster C (Health Administration; Score: 10.26) lacked clinical correlation, requiring problem-based learning. Cluster D (Family Health; Score: 9.50) suffered from an overly theoretical nature, making field visits and peer-mentoring necessary remedies. Conversely, Cluster B (Environmental Health; Score: 7.69) carried the lowest cognitive load, emerging as an ideal candidate for self-directed learning (SDL). Conclusion: Undergraduate medical students experience structural variations in curricular difficulty. These data-driven insights enable faculty to deploy targeted pedagogical interventions and optimize limited institutional resources.

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Published

2026-08-31

Issue

Volume 1, Issue 2, May-August 2026

Section

Original Article

References

1. National Medical Commission. Competency Based Undergraduate Curriculum for the Indian Medical Graduate [Internet]. New Delhi: Academic Cell, National Medical Commision; 2024 [cited 2026 May 19]. Available from: https://gemini.google.com/app/aefb8dbf23b647a0

2. Sulena S, Kulkarni A, Mathur M, Jyoti N, Sidhu TK, Badyal D, et al. Challenges in Implementing Competency-based Medical Education in India – Stakeholders’ Perspective: A Mixed-method Analysis. Int J Appl Basic Med Res. 2024;14(4):225–32. doi:10.4103/ijabmr.ijabmr_268_24 PubMed PMID: 39749169; PubMed Central PMCID: PMC11691106.

3. Sweller J. Cognitive load theory and educational technology. Educ Technol Res Dev. 2020 Feb 1;68(1):1– 16. doi:10.1007/s11423-019-09701-3

4. Malau-Aduli B, Ray R, O’Connor T, van der Kruk Y, Alele F, Bellingan M. Dealing with Academic Difficulty in Medical School: A Pilot Study. Educ Sci. 2020 Mar 23;10:83. doi:10.3390/educsci10030083

5. Dyrbye LN, Thomas MR, Shanafelt TD. Systematic review of depression, anxiety, and other indicators of psychological distress among U.S. and Canadian medical students. Acad Med J Assoc Am Med Coll. 2006 Apr;81(4):354–73. doi:10.1097/00001888-20060400000009 PubMed PMID: 16565188.

6. Weller S. Cultural Consensus Theory: Applications and Frequently Asked Questions. Field Methods - FIELD METHOD. 2007 Nov 1;19:339–68. doi:10.1177/1525822X07303502

7. Schensul J, LeCompte M. Essential Ethnographic Methods: A Mixed Methods Approach. 2013. doi:10.5040/9798216409496

8. Kneebone RL, Scott W, Darzi A, Horrocks M. Simulation and clinical practice: strengthening the relationship. Med Educ. 2004 Oct;38(10):1095–102. doi:10.1111/j.1365-2929.2004.01959.x PubMed PMID: 15461655.

9. Garro LY. Intracultural Variation in Folk Medical Knowledge: A Comparison Between Curers and Non-Curers. Am Anthropol. 2009 Oct 28;88:351–70. doi:10.1525/aa.1986.88.2.02a00040

10. Quinlan M. Considerations for Collecting Freelists in the Field: Examples from Ethobotany. Field Methods. 2005 Aug 1;17(3):219–34. doi:10.1177/1525822X05277460

11.C.Weller S, KimballRomney A. Systematic Data Collection [Internet]. SAGE Publications, Inc.; 1988 [cited 2026 May 25]. Available from: https://methods.sagepub.com/book/mono/systematic-data-collection/toc doi:10.4135/9781412986069

12.Borgatti S, Halgin D. On Network Theory [SSRN Scholarly Paper] [Internet]. Rochester, NY: Social Science Research Network; 2011 [cited 2026 May 25]. Available from: https://papers.ssrn.com/abstract=2260993 doi:10.2139/ssrn.2260993

13. Kruskal JB. Multidimensional scaling by optimizing goodness of fit to a nonmetric hypothesis. Psychometrika. 1964 Mar 1;29(1):1–27. doi:10.1007/BF02289565

14. Kruskal JB. Nonmetric multidimensional scaling: A numerical method. Psychometrika. 1964 Jun 1;29(2):115–29. doi:10.1007/BF02289694

15. Hout MC, Papesh MH, Goldinger SD. Multidimensional scaling. WIREs Cogn Sci. 2013;4(1):93–103. doi:10.1002/wcs.1203

16. Mair P, Groenen PJF, Leeuw J de. More on Multidimensional Scaling and Unfolding in R: smacof Version 2. J Stat Softw. 2022 May 13;102:1–47. doi:10.18637/jss.v102.i10

17. Althubaiti A. Attitudes of Medical Students Toward Statistics in Medical Research: Evidence From Saudi Arabia. J Stat Data Sci Educ. 2021 Jan 4;29(1):115–21. doi:10.1080/10691898.2020.1850220

18. Hannigan A, Hegarty AC, McGrath D. Attitudes towards statistics of graduate entry medical students: the role of prior learning experiences. BMC Med Educ. 2014 Apr 4;14:70. doi:10.1186/1472-6920-14-70 PubMed PMID: 24708762; PubMed Central PMCID: PMC4234395.

19. Stone DH. Public health in the undergraduate medical curriculum--can we achieve integration? J Eval Clin Pract. 2000 Feb;6(1):9–14. doi:10.1046/j.13652753.2000.00227.x PubMed PMID: 10807019.

20. Haque M, A Rahman NA, Rahman S, Majumder MdAA, Syed Mohdhar SSB, Lugova H, et al. Medical Students’ Perception Regarding Health Policy Teaching and Their Participation in Health Policy Roles: A Survey at a Public University in Malaysia. Healthcare. 2022 May 24;10(6):967. doi:10.3390/healthcare10060967 PubMed PMID: 35742019; PubMed Central PMCID: PMC9223141.

21. Gupta R. Why Should Medical Students Care about Health Policy? PLoS Med. 2006 Oct;3(10):e199. doi:10.1371/journal.pmed.0030199 PubMed PMID: 17076545; PubMed Central PMCID: PMC1621087.

22. Mukhopadhyay D, Bandyopadhyay K, Ray S. Sorting Out the Perception of Medical Teachers Regarding Family Adoption Program: A Qualitative Inquiry. Acta Medica Int. 2025 Apr;12(1):11. doi:10.4103/amit.amit_5_25

23. Kiran M, Hema NG. Perspectives of medical undergraduate students toward self-directed learning. Indian J Physiol Pharmacol. 2024 Jul 1;68(2):176–80. doi:10.25259/IJPP_30_2023

24. Aulakh J, Wahab H, Richards C, Bidaisee S, Ramdass PVAK. Self-directed learning versus traditional didactic learning in undergraduate medical education: a systemic review and meta-analysis. BMC Med Educ. 2025 Jan 16;25(1):70. doi:10.1186/s12909-024-06449-0 PubMed PMID: 39815233; PubMed Central PMCID: PMC11737201.