Surgical fellowship training in Canada: What is its current status and is improvement required?
Bibliographic record
Abstract
This paper examines current issues concerning surgical fellowship training in Canada. Other than information from a few studies of fellowship training in North America, there are scant data on this subject in the literature. Little is known about the demographic characteristics of those who pursue fellowship training in Canada, what the experiences and expectations are of fellows and their supervisors with respect to the strengths and weaknesses of this level of training, or how this level of education fits in with Canadian undergraduate and postgraduate medical training. We summarize current knowledge about fellowship training in Canada as it pertains to demographic characteristics, finances, work hours, residency training, preparation for clinical and research work and satisfaction with training. Most information on surgical fellowship training comes from the United States. As such, we used information from American studies to supplement the Canadian data. Because a surgical fellowship experience in Canada may be different from that in the United States, we propose that Canadian surgical fellows and their supervisors should be surveyed to gain an understanding of such information. This knowledge could be used to improve surgical fellowship training in Canada.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".