Influence of distributed medical education on pre-clerkship elective use and utility
Bibliographic record
Abstract
Purpose To explore differences in pre-clerkship elective (PCE) use and utility between main and regional campuses at McMaster University’s Michael G. DeGroote School of Medicine, in four main areas: ease of access to PCEs, volume and breadth of PCEs, helpfulness in choosing future specialties, and utility for clerkship preparation. Methods An anonymous and voluntary survey was distributed in early 2020 to McMaster University Michael G. DeGroote School of Medicine’s medical students across all three years of study. Data were analyzed for any significant differences between main and regional campuses, and post-hoc sensitivity analyses were used to account for non-response and self-selection bias. Results Regional campus students felt significantly less frustration around PCE availability (2.88 vs 4.16, p<0.001, scale 1(least) – 5(most)) and significantly greater ease of PCE scheduling than students at the main campus (3.50 vs 2.24, p<0.001, scale 1(least) – 5(most)). Regional campus students explored significantly fewer specialties (5.19 vs 6.19, p = 0.049) and there was no significant difference in the total number of PCE hours undertaken, nor hours spent with a single specialty. Overall, students in both campuses endorsed pressure to take PCEs and mixed benefits of PCEs for clerkship preparation. Students also found PCEs to be an important part of choosing a specialty independent of campus. Conclusion Regional medical campuses at McMaster University offer generally equal opportunities for PCE volume and breadth as main campuses, but with significantly lower barriers and frustrations around scheduling and availabilities. Conflicts of interest There are no conflicts of interest to report.
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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".