Research Productivity of Canadian Radiation Oncology Residents: A Time-Trend Analysis
Bibliographic record
Abstract
(1) Background: Research productivity is a mandatory component of Canadian radiation oncology (RO) resident training. To our knowledge, Canadian RO resident research publication productivity has not previously been analysed. (2) Methods: We compiled a 12-year database of RO residents in Canadian training programs who completed residency between June 2005 and June 2016. Resident names and dates of training were abstracted from provincial databases and department websites and were used to abstract data from PubMed, including training program, publication year, journal, type of research, topic and authorship position. Residents were divided into four time periods and the linear trend test evaluated publication rates over time. Univariable and multivariable logistic regression analyses were performed to identify authorship predictors. (3) Results: 227 RO residents representing 363 publications were identified. The majority were first-author publications (56%) and original research (77%). Overall, 82% of first-author, and 80% of any-author articles were published in resident year 4 or higher. Mean number of publications for first-author and any-author positions increased significantly over time (p = 0.016 and p = 0.039, respectively). After adjusting for gender and time period, large institutions (> 3 residents per year) trended toward associations with more first-author publications (odds ratio (OR): 2.44; p = 0.066) and more any-author publications (OR: 2.49; p = 0.052). No significant differences were observed by gender. (4) Conclusions: Canadian RO resident publication productivity nearly doubled over a 12-year period. The majority of publications are released in the last 2 years of residency, and larger residency programs may be associated with more publications. These findings serve as a baseline as programs transition to Competency Based Medical Education (CBME).
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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 | 0.007 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.013 | 0.033 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".