Allocation of Gastroenterology Training Positions in Ontario: Supply or Demand?
Bibliographic record
Abstract
Human Resources (HHR) Policy Branch for the Ontario Ministry of Health and Long-Term Care and a key member of the HealthForceOntario team.Among the responsibilities of Jeff's branch are HHR data and modelling, and physician and allied health planning.Jeff sits on the Postgraduate Management Committee of the Council of Ontario Faculties of Medicine together with the postgraduate medicine deans of Ontario's six medical schools and representatives of the Council of Ontario Universities.PA: At a time when demand for gastroenterology services seems to be exploding in Ontario, we heard this year that there would be no expansion in the training positions for gastroenterology.Can you explain to us the rationale behind this decision?JG: We have a lot more gastroenterology positions than we did a few years ago.The total number of gastroenterology postgraduate year 4 and postgraduate year 5 positions increased almost 80% from 19 positions in 2004 to 34 positions in 2006.Over the same time period, other subspecialties lost positions.For example, the number of geriatric medicine positions decreased by 25%.The Ministry of Health and Long-Term Care and the medical schools -through the Council of Ontario Faculties of Medicine -are working together to create a stable and predictable supply of medical trainees to meet the province's health care needs.This process is part of the HealthForceOntario strategy -a range of initiatives we are undertaking to ensure Ontarians can access qualified health care providers, now and in the future.PA: If there were more training positions in gastroenterology, there would be fewer people going into geriatrics.Would it not be better to increase incentives for geriatrics by improving their fee schedule?JG: There is no question we need to look at what makes some specialties less attractive than others.But I want to emphasize that creating incentives in one specialty will not necessarily encourage physicians to enter that area or strike the right balance in the long term.Compensation and lifestyle are two of the factors we can try to address.We can also increase exposure to the specialty and look at making curriculum changes.PA: As demand increases and supply fails to increase, there may be an increase in endoscopy services provided by physicians with substandard training.Ontarians older than 50 years of age
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.026 | 0.002 |
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".