Disentangling faculty development: A scoping review towards a rich description of the concept and its practice
Bibliographic record
Abstract
BACKGROUND: There is wide variation in how faculty development (FD) is practiced globally and described in the literature. This scoping review aims to clarify how FD is conceptualised and practiced in health professions education. METHODOLOGY: Using a systematic search strategy, 418 papers, published between 2015-2023, were included for full text review. We extracted data using closed and open-ended questions. Quantitative data were summarised using descriptive statistics and qualitative data synthesised using content analysis. RESULTS: was the most frequently used term encompassing a range of understandings and practices. Many papers focused on educators' enhanced understanding of teaching, learning, and assessment. Several highlighted the social context of collaborative practice and organisational learning. FD formats included workshops, courses, longitudinal programs, and coaching and mentoring. Dominant conceptual frameworks included Kirkpatrick's model of evaluation, communities of practice theory, adult learning theory, and experiential learning. CONCLUSIONS: Although FD continues to evolve in response to the dynamic HPE landscape, this growth needs to be accelerated. To facilitate meaningful collaboration across professions, contexts, and countries, attention must be paid to terms used and meanings ascribed to them. Those responsible for FD need to think anew about its purpose and practice, demonstrating flexibility as the ever-changing context demands.
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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 | 0.060 | 0.140 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.043 | 0.042 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".