Viewing faculty development through an organizational lens: Sharing lessons learned
Bibliographic record
Abstract
Faculty Development (FD) plays a key role in supporting education, especially during times of change. The effectiveness of FD often depends upon organizational factors, indicating a need for a deeper appreciation of the role of institutional context. How do organizational factors constrain or enhance the capacity of faculty developers to fulfil their mandates? Using survey research methodology, data from a survey of FD leaders at Canadian medical schools were analyzed using Bolman and Deal’s four frames: Symbolic, Political, Structural, and Human Resource (HR). In the Symbolic frame, FD leaders reported lack of identity as a FD unit, which was seen as a constraining factor. Within the Political frame, developing visibility was seen as an enhancing factor, though it did not always ensure being valued. In the Structural frame, expanding scope of practice was seen as an enhancing factor, though it could also be a constraining factor if not accompanied by increased resources. In the HR frame, a sense of instability due to changing leadership and uncertainty about human resources was seen as a constraining factor. While broadening the mandate of FD can generally be considered as positive, it is imperative that it is appropriately resourced and accompanied by recognition of FD as a valued contributor to the educational mission.
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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 | 0.017 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.007 |
| Science and technology studies | 0.012 | 0.031 |
| Scholarly communication | 0.018 | 0.029 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".