A national clinician–educator program: a model of an effective community of practice
Bibliographic record
Abstract
BACKGROUND: The increasing complexity of medical training often requires faculty members with educational expertise to address issues of curriculum design, instructional methods, assessment, program evaluation, faculty development, and educational scholarship, among others. DISCUSSION: In 2007, The Royal College of Physicians & Surgeons of Canada responded to this need by establishing the first national clinician-educator program. We define a clinician-educator and describe the development of the program. Adopting a construct from the business community, we use a community of practice framework to describe the benefits (with examples) of this program and challenges in developing it. The benefits of the clinician-educator program include: improved educational problem solving, recognition of educational needs and development of new projects, enhanced personal educational expertise, maintenance of professional satisfaction and retention of group members, a positive influence within the Royal College, and a positive influence within other Canadian academic institutions. SUMMARY: Our described experience of a social reorganization - a community of practice - suggests that the organizational and educational benefits of a national clinician-educator program are not theoretical, but real.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 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".