16. Postgraduate training and its effect on practice location, career choice and practice profile: Tracking 10 years of output from the University of Toronto
Bibliographic record
Abstract
The purpose of this study is to investigate the relationship between location and specialty of training and practice characteristics such as type of practice (i.e. community versus academic), socio-demographic profile of patients and their complexity, hospital/health facility affiliations and workload/productivity. The analysis required an extraction of registrant data from the University of Toronto Postgraduate Web Evaluation and Registration (POWER) system for a cohort of exiting residents and fellows from 1993 to 2003. The data extract was linked to several administrative databases held by ICES, including physician practice and billing information from the Ontario Health Insurance Plan (OHIP) and anonymized patient demographic data from the Registered Persons Database (RPDB). Results of this study will inform workforce policy issues such as the overall contribution made by Toronto graduates to Ontario, other Canadian provinces and international practice pool of physicians, trends regarding medical career choice, similarities and differences between career choices of International Medical Graduates versus Canadian Medical Graduates, impact of location/program of training, impact of length of training and profile/geography of patients served by graduates of Toronto. The study will aim to create a methodology/template for analysis that can be applied to other medical schools and catchment areas in human health resource planning. Chan B, Willett J. Factors Influencing Participation in Obstetrics by Obstetrician-Gynecologists. 2004; 103(3):493-498. Noble J, Baerlocher MO. Future Practice Profiles of Canadian Medical Trainees. Clinical and Investigative Medicine 2006; 29(4):288-289. Watson DE, Katz A, Reid RJ, Bogdanovic B, Roos N. Family Physician Workloads and Access to Care in Winnipeg: 1991 to 2001. Canadian Family Physician 2004; 171(4):339-342.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".