Embedding social accountability in the medical school and its curricula: Patan Academy of Health Sciences, Nepal
Bibliographic record
Abstract
Introduction: Patan Academy of Health Sciences (PAHS) was established in 2008 with a social accountability mandate and the mission to produce competent and committed health professionals to serve the rural and underserved population. Enrolment of undergraduate medical students started from 2010. This article describes the context and process for the establishment of the Academy, the approaches taken and some of the early outputs. Method: The information was collected from the policy documents, PAHS website, meeting minutes/ discussions, feedbacks and medial school records. All the information were compiled and presented under different headings/subheadings in a phase wise manner. Result: PAHS has been actively engaged in a multitude of partnerships from local to global and has chosen the best and most applicable innovations from around the world. The integrated suite of innovations the Academy has developed includes its admission policy, teaching-learning methodologies, community-based learning, scholarship-schemes and service bonds. The PAHS School of Medicine has successfully enrolled undergraduate medical students from all over the country, representing ethnic diversity, remote/rural background, underprivileged communities and gender balance. More than 50% graduates from the first five-batches are successfully deployed into primary level peripheral health facilities of the government health system. Conclusion: The initial reports and observations confirm that the integrated measures taken by the Academy have been effective in enrolling the right students, educating them in an effective way and deploying them to address the country's need. A longer follow-up on rural retention and performance evaluation is needed to conclusively establish the outcome of the school.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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 source (direct Gemma or distilled Codex), 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".