Measuring social accountability in health professional education: Development and international pilot testing of an evaluation framework
Bibliographic record
Abstract
BACKGROUND: Health professional schools are responsible for producing graduates with competencies and attitudes to address health inequities and respond to priority health needs. Health professional schools striving towards social accountability founded the Training for Health Equity Network (THEnet). AIM: This article describes the development of THEnet evaluation framework for socially accountable health professional education, presents the framework to be used as a tool by other schools and discusses the findings of pilot implementation at five schools. METHODS: The framework was designed collaboratively and built on Boelen and Woollard's conceptualization, production and usability model. It includes key components, linked to aspirational statements, indicators and suggested measurement tools. Five schools completed pilot implementation, involving workshops, document/data review and focus group discussions with faculty, students and community members. RESULTS: Three sections of the framework consider: How does our school work?; What do we do? and What difference do we make? Pilot testing proved that the evaluation framework was acceptable and feasible across contexts and produced findings useful at school level and to compare schools. The framework is designed as a formative exercise to help schools take a critical look at their performance and progress towards social accountability. Initiatives to implement the framework more widely are underway. The framework effectively aids in identifying strengths, weaknesses and gaps, with a view to schools striving for continuous self-improvement. CONCLUSION: THEnet evaluation framework is applicable and useful across contexts. It is possible and desirable to assess progress towards social accountability in health professional schools and this is an important step in producing health professionals with knowledge, attitudes, and skills to meet the challenges of priority health needs of underserved populations.
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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 | 0.292 | 0.200 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".