Increasing Trends in Orthopedic Fellowships Are Not due to Inadequate Residency Training
Bibliographic record
Abstract
Orthopedic residents have one of the highest fellowship participation rates among medical specialities and there are growing concerns that inadequate residency training may be contributing to this trend. Therefore, a mixed-exploratory research survey was distributed to all 148 graduating Canadian orthopedic residents to investigate their perceptions and attitudes for pursuing fellowships. A response rate of 33% ( n=49 ) was obtained with the majority of residents undertaking one (27%) or two (60%) fellowships. Surgical-skill development was reported as the most common motivating factor, followed by employment and marketability; malpractice protection and financial reasons were the least relevant. The overwhelming majority of residents (94%, n=46 ) felt adequately prepared by their residency training for independent general practice, and 84% ( n=41 ) of respondents did not feel that current fellowship trends were due to poor residency training. Three common themes were expressed in their comments: the growing perceived expectation by healthcare professionals and employers to be fellowship-certified, the integration of fellowship training into the surgical education hierarchy, and the failure of residency training curriculums to accommodate for this trend. In conclusion, Canadian orthopedic residents are confident of their residency training and are increasingly pursuing fellowships to primarily develop their surgical skills and expertise.
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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 | 0.004 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".