<p>Faculty development program evaluation: a need to embrace complexity</p>
Bibliographic record
Abstract
Faculty development is essential for renewing and assisting faculty to maintain teaching effectiveness and adapt to innovations in Health Professions educational institutions. The evaluation of faculty development programs appears to be a significant step in maintaining its relevance and efficiency. Yet, little has been published on the specific case of faculty development program evaluation in spite of the availability of general program evaluation models. These models do not measure or capture the information educators want to know about outcomes and impacts of faculty development. We posit that two reasons account for this. The first is the evolving nature of faculty development programs as they adapt to current reforms and innovations. The second involves the limitations imposed by program evaluation models that fail to take into account the multiple and unpredictable outcomes and impacts of faculty development. It is generally accepted that the outcomes and impacts are situated at various levels, ranging from the individual to the institutional and cultural levels. This calls for evaluation models that better capture the complexity of the impacts of faculty development, in particular the reciprocal relationships between program components and outcomes. We suggest conceptual avenues, based on Structuration Theory, that could lead to identifying the multilevel impacts of faculty development.
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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 | 0.213 | 0.335 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.017 | 0.013 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".