Association of physician payment model and team-based care with timely access in primary care: a population-based cross-sectional study
Bibliographic record
Abstract
BACKGROUND: It is unclear how patient-reported access to primary care differs by physician payment model and participation in team-based care. We examined the association between timely and after-hours access to primary care and physician payment model and participation in team-based care, and sought to assess how access varied by patient characteristics. METHODS: We conducted a cross-sectional analysis of adult (age ≥ 16 yr) Ontarians who responded to the Ontario Health Care Experience Survey between January 2013 and September 2015, reported having a primary care provider and agreed to have their responses linked to health administrative data. Access measures included the proportion of respondents who reported same-day or next-day access when sick, satisfaction with time to appointment when sick, telephone access and knowledge of an after-hours clinic. We tested the association between practice model and measures of access using logistic regression after stratifying for rurality. RESULTS: A total of 33 665 respondents met our inclusion criteria. In big cities, respondents in team and nonteam capitation models were less likely to report same-day or next-day access when sick than respondents in enhanced fee-for-service models (team capitation 43%, adjusted odds ratio [OR] 0.88, 95% confidence interval [CI] 0.79-0.98; nonteam capitation 39%, adjusted OR 0.78, 95% CI 0.70-0.87; enhanced fee-for-service 46% [reference]). Respondents in team and nonteam capitation models were more likely than those in enhanced fee-for-service models to report that their provider had an after-hours clinic (team capitation 59%, adjusted OR 2.59, 95% CI 2.39-2.81; nonteam capitation 51%, adjusted OR 1.90, 95% CI 1.76-2.04; enhanced fee-for service 34% [reference]). Patterns were similar for respondents in small towns. There was minimal to no difference by model for satisfaction with time to appointment or telephone access. INTERPRETATION: In our setting, there was an association between some types of access to primary care and physician payment model and team-based care, but the direction was not consistent. Different measures of timely access are needed to understand health care system performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".