Bibliographic record
Abstract
Research training equips residents with the skills to consume and produce research evidence and deliver evidence-based care. Within Physical Medicine and Rehabilitation, studies have historically demonstrated low rates of resident research productivity. Although Canadian residency requirements mandate research participation, little is known about Canadian residents' research productivity. Using standard systematic review search strategies, we evaluated the rate and type of peer-reviewed publications produced by resident physicians during postgraduate medical training for a historic cohort of Physical Medicine and Rehabilitation residents who successfully passed the Canadian Royal College Fellowship examination in 2015, 2016, and 2017 (N = 74). Resident physicians produced 62 peer-reviewed publications during the study period. A total of 43.2% of resident physicians produced at least one such publication and 20.3% produced more than one. The resident physician was the first author for 51.6% of publications. Reviews were the most frequent publication type (19.4%), followed by observational studies (16.1%) and case reports (16.1%). Musculoskeletal conditions (11.3%) and stroke (9.7%) were the most frequent areas of study. Most publications were in nonrehabilitation journals. These findings demonstrate modest research productivity despite mandatory research participation; although research productivity is higher than in previous cohorts, publications of convenience, such as reviews and case reports, are similarly frequent.
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 | 0.015 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.024 | 0.009 |
| Open science | 0.004 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.642 | 0.603 |
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".