Does medical school research productivity predict a resident's research productivity during residency?
Bibliographic record
Abstract
Background Research productivity is an important component of the CanMEDS Scholar role and is an accreditation requirement of Canadian Otolaryngology training programs. Our objective was to determine if an association exists between publication rates before and during Otolaryngology residency. Methods We obtained the names for all certified Canadian Otolaryngologists who graduated between 1998 and 2013 inclusive, and conducted a Medline search for all of their publications. Otolaryngologists were subgrouped based on year of residency graduation and the number of articles published pre-residency and during residency (0 or ≥1). Chi-squared analyses were used to evaluate whether publications pre-residency and year of graduation were associated with publications during residency. Results We obtained data for 312 Canadian Otolaryngologists. Of those 312 graduates, 46 (14.7%) had no identifiable publications on PubMed and were excluded from the final data analysis. Otolaryngology residents had a mean 0.65 (95% CI 0.50-0.80) publications before residency and 3.35 (95% CI 2.90-3.80) publications during residency. Between 1998 and 2013, mean publication rates before and during residency both increased significantly ( R2 = 0.594 and R2 = 0.759, respectively), whereas publication rates after residency graduation has stagnated ( R2 = 0.023). The odds of publishing during residency was 5.85 times higher (95% CI 2.69-12.71) if a resident published prior to residency ( p < 0.0001). The Spearman correlation coefficient between publications before and during residency is 0.472 ( p < 0.0001). Conclusion Residents who publish at least one paper before residency are nearly six times as likely to publish during residency than those who did not publish before residency. These findings may help guide Otolaryngology program selection committees in ranking the best CaRMS candidates.
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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 | 0.012 | 0.085 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".