Setting Priorities for Teaching and Learning: An Innovative Needs Assessment for a New Family Medicine Program in Lao PDR
Bibliographic record
Abstract
Lao People's Democratic Republic (Lao PDR) is a small, tropical, landlocked country in southeast Asia. It is one of the least developed countries in the region, and its socioeconomic indicators are among the lowest 25% in the world. The World Health Organization has long called for increased equity in primary health care access around the world. To meet this need in Lao PDR, the Family Medicine Specialist Program was developed, a Lao-generated postgraduate training program designed to produce community-oriented primary care practitioners to serve the rural, remote areas of Lao PDR, where 80% of the population lives. An innovative method of needs assessment was required to determine the health care priorities to be met by this new program. Through the use of a modified Delphi technique, local key leaders in medical education, clinical specialists, and teachers were consulted to develop prioritized objectives for the hospital-based curriculum of the program. By setting priorities for teaching and learning in the unique and needy circumstances of Lao PDR, a novel approach to curriculum planning in a low-income country was explored and ultimately formed the foundation of the new curriculum. This process served to direct the allocation of scarce resources during implementation of this groundbreaking program. More importantly, this model of needs assessment could potentially be used to customize medical curricula in other low-income countries facing challenges similar to those in Lao PDR.
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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 | 0.029 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".