Faculty Development Effectiveness: Insights from a Program Evaluation
Bibliographic record
Abstract
Background. Faculty development programs are often time and resource intensive. In order to accommodate time constrained clinicians a limited time commitment faculty development program was developed and was shown to be effective in improving participant’s scholarly productivity. Objectives. The objective of this study was to assess participants’ perceptions of why the faculty development program was effective in promoting scholarship in education. Methods. In-depth semistructured interviews of course participants were conducted a year after completing a faculty development program. The interviews were audiotaped and transcribed verbatim. The transcriptions were coded independently by the investigators for dominant themes. The investigators held coding meetings to further refine the themes and discrepancies were handled by referring to the transcripts and reaching consensus. Results. The participants’ satisfaction with the course as described in the interviews correlated with the early satisfaction surveys. Reasons offered for this impact fell into four broad categories: course content, course format, social networking during the course, and the course facilitation coaching strategies to achieve goals. Conclusions. Course focusing on the process, experiential learning, and situating the course facilitator in the role of a functional mentor or coach to complete projects can be effective in facilitating behaviour change after faculty development 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 | 0.083 | 0.094 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".