Faculty development for implementation of an EPA-based program
Bibliographic record
Abstract
Faculty development, trainee orientation and stakeholder engagement are essential elements of change management in the implementation of EPAs. An effective strategy addresses stakeholder needs over the various stages of planning, piloting, and implementation of EPAs. It encompasses faculty and all other stakeholders, i.e., clinical supervisors and assessors, non-workplace-based teaching faculty, coaches or advisers for trainees’ portfolios, members of clinical competence committees, administrators, program directors and other leaders. Best practices involve engaging with stakeholders as essential partners working toward a shared vision, building a sense of a community of practice, planning a range of activities in a continuous, dynamic, and enabling process, and including trainee development alongside faculty development. This chapter introduces evolving conceptions of faculty development and identifies key principles and strategies to guide the design of an effective plan. A range of approaches is outlined from passive to active, with various modes of delivery including face-to-face and hybrid and self-directed learning. Factors to consider are discussed and the significance of context is acknowledged. The importance of resourcing faculty and other stakeholders and the need to make a business case supported by ongoing evaluation are highlighted. Three examples of strategies in practice illustrate some key ideas. An analysis of the specific needs of different stakeholder groups, with potential approaches and a directory of accessible digital resources to support faculty development, trainee orientation, and engagement with other stakeholders, is also provided.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.046 | 0.011 |
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".