Preceptor engagement in distributed medical school campuses
Bibliographic record
Abstract
BACKGROUND: There is increasing interest in distributed medical campuses and engagement of physicians in these communities. To date, there has been suboptimal recruitment of physicians to participate in medical education at distributed campuses. The purpose of this project was to identify barriers to engagement in medical education by community physicians in the geographical catchment of the Waterloo Regional Campus of McMaster. METHOD: In-depth, semi-structured, qualitative interviews were conducted with physicians not involved in teaching. Interview recordings were transcribed and analyzed using a closed-loop, iterative coding methodology and thematic analysis was performed. Interviews were conducted until thematic saturation was achieved. RESULTS: Six interviews were conducted and coded. Nine key themes emerged: academic centre versus distributed sites, interest in teaching, financial considerations, administrative barriers, medical experience and knowledge currency, practice environment and schedule, training on teaching, setting up systems for learners in distributed campus settings, and student engagement and medical learner level. CONCLUSIONS: Barriers to engagement in teaching primarily focused on differences in job structure in the community, administrative barriers both at the hospital and through the medical school, and lack of knowledge on how to teach. As medical schools look to expand the capacity of distributed campuses, misperceptions should be addressed and opportunities to improve engagement should be further explored.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.014 | 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".