Patient characteristics associated with enrolment under voluntary programs implemented within fee-for-service systems in British Columbia and Quebec: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: There is a paucity of information on patient characteristics associated with enrolment under voluntary programs (e.g. incentive payments) implemented within fee-for-service systems. We explored patient characteristics associated with enrolment under these programs in British Columbia and Quebec. METHODS: We used linked administrative data and a cross-sectional design to compare people aged 40 years or more enrolled under voluntary programs to those who were eligible but not enrolled. We examined 2 programs in Quebec (enrolment of vulnerable patients with qualifying conditions [implemented in 2003] and enrolment of the general population [2009]) and 3 in BC (Chronic disease incentive [2003], Complex care incentive [2007] and enrolment of the general population [A GP for Me, 2013]). We used logistic regression to estimate the odds of enrolment by neighbourhood income, rural versus urban residence, previous treatment for mental illness, previous treatment for substance use disorder and use of health care services before program implementation, controlling for characteristics linked to program eligibility. RESULTS: In Quebec, we identified 1 569 010 people eligible for the vulnerable enrolment program (of whom 505 869 [32.2%] were enrolled within the first 2 yr of program implementation) and 2 394 923 for the general enrolment program (of whom 352 380 [14.7%] were enrolled within the first 2 yr). In BC, we identified 133 589 people eligible for the Chronic disease incentive, 47 619 for the Complex care incentive and 1 349 428 for A GP for Me; of these, 60 764 (45.5%), 28 273 (59.4%) and 1 066 714 (79.0%), respectively, were enrolled within the first 2 years. The odds of enrolment were higher in higher-income neighbourhoods for programs without enrolment criteria (adjusted odds ratio [OR] comparing highest to lowest quintiles 1.21 [95% confidence interval (CI) 1.20-1.23] in Quebec and 1.67 [95% CI 1.64-1.69] in BC) but were similar across neighbourhood income quintiles for programs with health-related eligibility criteria. The odds of enrolment by urban versus rural location varied by program. People treated for substance use disorders had lower odds of enrolment in all programs (adjusted OR 0.60-0.72). Compared to people eligible but not enrolled, those enrolled had similar or higher numbers of primary care visits and longitudinal continuity of care in the year before enrolment. INTERPRETATION: People living in lower-income neighbourhoods and those treated for substance use disorders were less likely than people in higher-income neighbourhoods and those not treated for such disorders to be enrolled in programs without health-related eligibility criteria. Other strategies are needed to promote equitable access to primary care.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".