Medical students’ challenges and suggestions regarding research training: a synthesis of comments from a cross-sectional survey
Bibliographic record
Abstract
BACKGROUND: We previously reported on a cross-sectional study of students from the Michael G. DeGroote School of Medicine at McMaster University that found most respondents wanted more opportunities to participate in research. Students provided additional comments that we synthesized to enrich the findings of our quantitative analysis. METHODS: From our previously administered 13-item, online questionnaire, run across three campuses in Ontario, Canada, 498 of 618 medical students completed our survey and 360 (72%) provided optional written comments, which we synthesized using thematic analysis in this current study. RESULTS: Major themes that emerged were: (1) Active student participation to identify research opportunities and interested mentors are needed; (2) Types of research involvement; (3) Uncertainty whether research training translates into useable skills; (4) Desire for a formalized research curriculum and centralization of research opportunities across campuses. CONCLUSION: Programs should stress to interested students the importance of actively looking for research opportunities and consider both large and small-group educational sessions.
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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 | 0.037 | 0.119 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".