Predictors of medical student interest and confidence in research during medical school.
Bibliographic record
Abstract
BACKGROUND: 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. METHODS: 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 an electronic survey and two subsequent reminder e-mails. RESULTS: The response rate was 81% (498 of 618). Most (n=445, 89%) had 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, the most significant predictors of higher self-rated research ability were prior thesis work and other prior research experience. CONCLUSION: 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.
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 | 0.006 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".