Association of pre-residency publications with research productivity in residency, fellowship, and academic career choice among Canadian ophthalmologists
Bibliographic record
Abstract
OBJECTIVE: This study aimed to assess whether the research output of medical students who matched into a Canadian ophthalmology residency program influences their subsequent research productivity during residency, decision to pursue a fellowship, or engagement in academic practice. DESIGN: Retrospective database review. PARTICIPANTS: A total of 369 trainees commencing ophthalmology residency from 2004 to 2015 at 15 residency programs. METHODS: Each trainee's publication record was queried in Scopus before and after the date they started residency. Multiple public sources were searched to identify fellowship placement and the type of subsequent practice (i.e., academic or community). Predictors of research productivity during residency, fellowship, and practice setting were assessed using multivariable regression analyses. RESULTS: Trainees with pre-residency publications (n = 187) demonstrated significantly higher research productivity during residency than those without pre-residency publications (n = 182), with a mean of 5.17 ± 5.97 versus 1.60 ± 2.38 publications on any topic (p < 0.001). Pre-residency research output was a predictor of research productivity during residency (relative risk = 1.17; 95% CI, 1.09-1.27; p < 0.001), pursuing fellowship (odds ratio, 2.9; 95% CI, 1.74-4.83), and an academic career (odds ratio = 1.85; 95% CI, 1.07-3.2). CONCLUSION: Pre-residency research output is a significant predictor of research productivity during residency and subsequent career choices, suggesting that pre-residency publishing reflects a propensity toward an academic trajectory. Residency publication count moderates this association, underscoring the role of the residency program environment in fostering research productivity. Addressing barriers such as mentorship, funding, and curriculum may be key to incentivizing trainees to pursue academic medicine.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".