Re-positioning faculty development as knowledge mobilization for health professions education
Bibliographic record
Abstract
Faculty development as knowledge mobilization offers a particularly fruitful and novel avenue for exploring the research-practice interface in health professions education. We use this 'eye opener' to build off this assertion to envision faculty development as an enterprise that provides a formal, recognized space for the sharing of research and practical knowledge among health professions educators. Faculty development's knowledge mobilizing strategies and outcomes, which draw upon varied sources of knowledge, make it a potentially effective knowledge mobilization vehicle.First, we explain our choice of the term knowledge mobilization over translation, in an attempt to resist the false dichotomy of 'knowledge user' and 'knowledge creator'. Second, we leverage the documented strengths of faculty development against the documented critiques of knowledge mobilization in the hopes of avoiding some of the pitfalls that have befallen previous attempts at closing knowing-doing gaps.Through faculty development, faculty are indeed educated, in the traditional sense, to acquire new knowledge and skill, but they are also socialized to go on to form the systems and structures of their workplaces, as leaders and workers. Therefore, faculty development can not only mobilize knowledge, but also create knowledge mobilizers. Achieving this vision of faculty development as knowledge mobilization requires an acceptance of multiple sources of knowledge, including practice-based knowledge, and of multiple purposes for education and faculty development, including professional socialization.
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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 | 0.024 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.080 |
| Scholarly communication | 0.027 | 0.019 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".