Curated Collections for Educators: Five Key Papers about Program Evaluation
Bibliographic record
Abstract
The evaluation of educational programs has become an expected part of medical education. At some point, all medical educators will need to critically evaluate the programs that they deliver. However, the evaluation of educational programs requires a very different skillset than teaching. In this article, we aim to identify and summarize key papers that would be helpful for faculty members interested in exploring program evaluation. In November of 2016, the 2015-2016 Academic life in emergency medicine (ALiEM) Faculty Incubator program highlighted key papers in a discussion of program evaluation. This list of papers was augmented with suggestions by guest experts and by an open call on Twitter. This resulted in a list of 30 papers on program evaluation. Our authorship group then engaged in a process akin to a Delphi study to build consensus on the most important papers about program evaluation for medical education faculty. We present our group's top five most highly rated papers on program evaluation. We also summarize these papers with respect to their relevance to junior medical education faculty members and faculty developers. Program evaluation is challenging. The described papers will be informative for junior faculty members as they aim to design literature-informed evaluations for their educational programs.
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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 | 0.035 | 0.190 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.045 | 0.037 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.086 | 0.024 |
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".