Rural Community as Context and Teacher for Health Professions Education
Bibliographic record
Abstract
Nepal is a low-income, landlocked country located on the Indian subcontinent between China and India. The challenge of finding human resources for rural community health care settings is not unique to Nepal. In spite of the challenges, the health sector has made significant improvement in national health indices over the past half century. However, in terms of access to and quality of health services and impact, there remains a gross urban-rural disparity. The Patan Academy of Health Sciences (PAHS) has adopted a community-based education model, termed "community based learning and education" (CBLE), as one of the principal strategies and pedagogic methods. This method is linked to the PAHS mission of improving rural health in Nepal by training medical students through real-life experience in rural areas and developing a positive attitude among its graduates towards working in rural areas. This article outlines the PAHS approach of ruralizing the academy, which aligns with the concept of community engagement in health professional education. We describe how PAHS has embedded medical education in rural community settings, encouraging the learning context to be rural, fostering opportunities for community and peripheral health workers to participate in teaching-learning as well as evaluation of medical students, and involving community people in curriculum design and implementation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".