The role of collaboration and mentorship in the publication of surgical resident research
Bibliographic record
Abstract
Background: Research is an integral part of surgical training and a mandated competency by national accreditation bodies. Most residents engage in research, but the conversion of this research into peer-reviewed publications is unknown. The objectives of this study were to assess the conversion rate of resident research into published manuscripts and determine what variables predict publication. Methods: Through a retrospective design, 99 resident research abstracts were identified from the Surgery Research Day at the University of Saskatchewan 2008-2018. Publication status was verified using Google Scholar and PubMed. Variables associated with resident-specific, mentor-specific, and project-specific variables were assessed for their role in predicting publication. Results: Fifty-two (53%) of the 99 abstracts were published in a peer-reviewed journal, and 43 (43%) were presented at a national conference. Logistic regression analysis revealed multidisciplinary research (OR 4.46, CI 1.8-11.4, p = 0.002), projects involving multiple resident researchers (OR 2.56, CI 1.02-6.43, p = 0.045), and faculty supervisor having > 25 publications (OR 2.46, CI 1.03-5.88, p = 0.042) as significant predictors of publication. Conclusions: Our study identifies three variables related to collaboration and mentorship that can serve as potential starting points to increase research productivity amongst medical trainees.
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 | 0.089 | 0.455 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".