Financial and Work Satisfaction: Impacts of Participation in Primary Care Reform on Physicians in Ontario
Bibliographic record
Abstract
Governments in ontario have promised family physicians (fps) that participation in primary care reform would be financially as well as professionally rewarding.We compared work satisfaction, incomes and work patterns of fps practising in different models to determine whether the predicted benefits to physicians really materialized.study participants included 332 fps in ontario practising in five models of care.The study combined self-reported survey data with administrative data healThcare policy Vol.5 No.2, 2009 [e163] Financial and Work Satisfaction: Impacts of Participation in Primary Care Reform on Physicians in Ontariofrom ices and income data from the canada revenue agency.fps working in non-fee-for-service (ffs) models had higher levels of work satisfaction than those in ffs models.incomes were similar across groups prior to the advent of primary care reform.incomes of family health network fps rose by about 30%, while family health group fps saw increases of about 10% and those in ffs experienced minimal changes or decreases.self-reported change in income was not reliable, with only 47% of physicians correctly identifying whether their income remained stable, increased or decreased.The availability of a variety of ffs-and non-ffs-based payment options, each designed to accommodate physicians with different types or styles of practice, may be a useful tool for governments as they grapple with issues of physician recruitment and retention.Enhanced access (mandatory after-hours access and on-call) Support for multidisciplinary team approaches Support for enhanced information technology Non-FFS payment Payment method
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".