Primary Care Physician Supply and Children's Health Care Use, Access, and Outcomes: Findings From Canada
Bibliographic record
Abstract
OBJECTIVES: To describe the relationship of primary care physician (PCP) supply for children and measures of health care access, use, and outcomes. METHODS: We conducted a population-based, cross-sectional study of all Ontario children from 2003 to 2005. We used health administrative data to calculate county-level supply (full-time equivalents [FTEs]) of PCPs. We modeled the relationship of supply to (1) recommended primary care visits, (2) emergency department (ED) use, and (3) ambulatory care-sensitive condition admissions and adjusted for neighborhood income. We used population-based surveys to describe access. RESULTS: The county-level PCP supply ranged from 1720 to 4720 children per FTE. Of the children, 45.4% live in the highest-supply areas (<2000 children per FTE) and 8% in the lowest-supply areas (>3000 children per FTE). Compared with high-supply counties, the lowest had significantly lower rates of primary care visits (2716 vs 7490 per 1000) and higher proportions of newborns without early follow-care (58.2% vs 14.5%). Low-supply areas had higher rates of ED visits (440 vs 179 per 1000) and admissions. A stepwise gradient existed for every decrease in supply for most measures. Self-reported access barriers were most evident in areas with >3500 children per FTE (32.8% without a physician). CONCLUSIONS: Under universal insurance there are differences in access to, and outcomes of, primary care related to local physician supply after controlling for neighborhood income. The most pronounced effect is on primary and ED care use, but there are implications for acute and chronic disease control. Physician distribution is a critical issue to address in policies to improve access to care.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.006 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".