Predictors of medical student interest and confidence in research during training: a cross-sectional study
Bibliographic record
Abstract
Purpose: Research education and opportunities are an important part of undergraduate medical education. This study’s objectives were to determine students’ interest in research, student self-rated research skills and to assess potential predictors of research interest and confidence. Method: Stakeholder consultation and literature informed a 13-item cross-sectional survey. In 2014, all students enrolled in McMaster University’s School of Medicine in Ontario, Canada were sent three waves of an electronic survey. Results: The response rate was 81% (498 of 618). Most (n=445, 89%) endorsed prior research experiences. The majority of students (n=383, 86%) wanted more research education and opportunities. Higher rating of their supervisors’ understanding of research was associated with greater interest in research (OR=2.08; 95% CI=1.27–3.41). Home campus (distributed vs. main) was not a significant predictor of research interest. In our adjusted linear regression model, significant predictors of higher self-rated research ability included prior thesis work and higher self-rated knowledge gained in MD program. Conclusions: In a survey of a three-year medical school, medical student interest in further research education and opportunities was high and positively predicted by student-rated supervisors’ understanding of research, but not campus location. This study also identified several predictors of student self-rated research ability.
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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 | 0.004 | 0.010 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".