The ties that bind: a network approach to creating a programme in faculty development
Bibliographic record
Abstract
CONTEXT: Current trends in medical education reflect the changing health care environment. An increasingly large and diverse student population, a move to more distributed models of education, greater community involvement and an emphasis on social accountability, interprofessional education and student-centred approaches to learning necessitate new approaches to faculty development to help faculty members respond effectively to this rapidly changing landscape. METHODS: Drawing upon the tenets of network theory and the broader organisational literature, we propose a 'fishhook' model of faculty development programme formation. The model is based on seven key factors which supported the successful formation of a centralised programme for faculty development that addressed many of the contemporary issues in medical education. These factors include: environmental readiness; commitment and vision of a mobiliser; recruitment of key stakeholders and leaders to committees; formation of a collaborative network structure; accumulation of networking capital; legitimacy, and flexibility. DISCUSSION: Our aim in creating this model is to provide a guide for other medical schools to consider when developing similar programmes. The model can be adapted to reflect the local goals, settings and cultures of other medical education contexts.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.010 | 0.023 |
| Scholarly communication | 0.013 | 0.014 |
| Open science | 0.004 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".