Five ways to get a grip on applying a program evaluation model in health professions education academies
Bibliographic record
Abstract
The proliferation of health professions educator academies across Canada and the United States illustrates the value they hold for faculty and institutions. Yet, establishing and evaluating the efficacy of them through program evaluation can be challenging. Moreover, academy leadership often lack the time, bandwidth skillset and personnel to undertake rigorous program evaluation efforts. We outline a step-by-step guide for getting a grip on evaluating health professions educator academies. Developing a plan for program evaluation in advance of any new academy initiative helps to ensure the academy calibrates and re-calibrates to accomplish outcomes and meet stakeholder expectations. It also provides a mechanism for tracking academy impact, which strengthens requests for funding, promotes sustainability and encourages continued buy-in and support from institutional stakeholders. For all of these reasons, we present the following recommendations: apply the relevant program evaluation framework(s); identify resources for program evaluation; prepare to tell your academy's story; list desired program outcomes; establish a data collection plan; and obtain institutional review board approval.
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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 | 0.586 | 0.475 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.015 |
| Science and technology studies | 0.023 | 0.053 |
| Scholarly communication | 0.056 | 0.065 |
| Open science | 0.011 | 0.030 |
| Research integrity | 0.024 | 0.041 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".