Practice patterns of graduates of a CCFP(EM) residency program
Bibliographic record
Abstract
Objective To determine the practice settings of graduates of a residency program that leads to a Certificate of Special Competence in Emergency Medicine (CCFP[EM]). Design Web-based survey using standard Dillman methodology. Setting Canada. Participants All graduates of the CCFP(EM) residency training program at the University of Toronto (U of T) in Ontario between 1982 and 2009. Main outcome measures Practice type and location, job satisfaction, and nonclinical EM activities of graduates of a CCFP(EM) residency program. Results Of 146 graduates surveyed, 88 responded (response rate of 60.3%). All of the respondents indicated that they had practised EM at some point after completing the CCFP(EM) program at U of T. At survey completion, 76.7% were practising EM. Of the EM-practising cohort, 93.9% worked in urban or suburban hospitals as opposed to rural settings. Those practising EM expressed high levels of job satisfaction, with 83.3% reporting a score of 8 or higher on a 10-point satisfaction scale. Most (57.0%) of the graduates of the CCFP(EM) residency program at U of T had participated in leadership activities in EM on local, provincial, or national levels. Conclusion Most graduates of the CCFP(EM) residency program continue to practise EM, and most of them practise in urban and suburban environments. The low attrition rate of CCFP(EM) graduates should be regarded as a success of the CCFP(EM) program, and the geographic distribution of all physicians, including EM providers, warrants further study to help plan future physician resources in Canada.
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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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".