Bibliographic record
Abstract
Participatory evaluation involves a partnership between program evaluators and stakeholders. This paper provides tips for planning and conducting a participatory evaluation of a medical education program. The tips highlight the need to recognize the importance of judgment in participatory evaluation, assess the appropriateness of participatory evaluation for the setting, determine a predominant stream of participatory evaluation, and select stakeholders for participation carefully. The tips also suggest that one should initiate participation at the program planning stage, engage a participatory evaluator, develop an evaluation framework, associate participatory evaluation with more than just qualitative methods, and use technology to facilitate participation. Furthermore, the tips illuminate that while individuals can use participatory evaluation to build evaluation capacity, it is important that they use three dimensions (i.e. control of decision-making, stakeholder selection, depth of participation) for determining the level of "participatory-ness," as well as publish and reflect on their use of participatory evaluation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.151 | 0.184 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.003 | 0.012 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.013 | 0.007 |
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".