Multifaceted Support for a New Medical School in Nepal Devoted to Rural Health by a Canadian Faculty of Medicine and Dentistry
Bibliographic record
Abstract
Nepal and Alberta are literally a world apart. Yet they share a common problem of restricted access to health services in remote and rural areas. In Nepal, urban-rural disparities were one of the main issues in the recent civil war, which ended in 2006. In response to the need for improved health equity in Nepal a dedicated group of Nepali physicians began planning the Patan Academy of Health Sciences (PAHS), a new health sciences university dedicated to the education of rural health providers in the early 2000s. Beginning with a medical school the Patan Academy of Health Sciences uses international help to plan, deliver and assess its curriculum. PAHS developed an International Advisory Board (IAB) attracting international help using a model of broad, intentional recruitment and then on individuals' natural attraction to a clear mission of peace-making through health equity. Such a model provides for flexible recruitment of globally diverse experts, though it risks a lack of coordination. Until recently, the PAHS IAB has not enjoyed significant or formal support from any single international institution. However, an increasing number of the international consultants recruited by PAHS to its International Advisory Board are from the University of Alberta in Edmonton, Alberta, Canada (UAlberta). The number of UAlberta Faculty of Medicine and Dentistry members involved in the project has risen to fifteen, providing a critical mass for a coordinated effort to leverage institutional support for this partnership. This paper describes the organic growth of the UAlberta group supporting PAHS, and the ways in which it supports a sister institution in a developing nation.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.009 | 0.002 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.041 | 0.004 |
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".