Access to family physicians in southwestern ontario.
Bibliographic record
Abstract
OBJECTIVE: Shortages of family physicians (FPs) have been reported, but accurate data on the scope of this problem are sparse. The study objective was to determine the proportion of the population in southwestern Ontario without access to a regular FP and sources of usual medical care for individuals with and without a regular FP. METHOD: Random-digit dialling was used to obtain a stratified, random sample of households from 10 counties in southwestern Ontario, which resulted in 1,387 participants (60.5% cooperation rate). Adults reported on themselves, while a random selection of parents reported on their children, yielding data on individuals ranging from 0 to 95 years of age. RESULTS: 9.1% (95% CI = 7.8% to 10.6%) of individuals did not have a regular FP. Most individuals without a regular FP used walk-in clinics (55%) or emergency rooms (13%) as their usual source of care, while 5.9% reported not receiving medical care. Lack of physicians accepting new patients was the most common reason for not having a regular FP (27%), although some individuals chose not to have one (9.9%) or had alternative access to care (13.2%). CONCLUSIONS: Based on the assumption that the individuals who chose not to have a FP, or who had access to alternative care, would continue not to want a FP if one were available, we estimate that 5.1% of the population of southwestern Ontario requires a FP. The health implications of not having a regular FP in Canada need to be examined.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".