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.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.023 | 0.513 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.026 | 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 teacher head, 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".