Measuring the effect of Family Medicine Group enrolment on avoidable visits to emergency departments by patients with diabetes in Quebec, Canada
Bibliographic record
Abstract
The Family Medicine Group (FMG) model of primary care in Quebec, Canada, was driven by the voluntary implementation of family physicians. Our main objective was to measure the effect of FMG enrolment on avoidable use of the emergency department (ED) by diabetic patients. We also sought to determine if effects differed according to whether patients were infrequent or frequent users of the ED and according to high- versus low-regional levels of enrolment. We used data from provincial health administrative databases to identify the diabetic patient population over the age of 20 years for each fiscal year between 2003-2004 and 2011-2012. We used fixed effects and marginal structural models to estimate the effect of enrolment in FMGs on avoidable use of the ED. Our results indicated that for every 10-percentage point increase in the population enrolled with an FMG in the year prior to an event, there was a 3% reduction in avoidable visits to the ED made by an individual (RR = 0.97; 95% CI = 0.95, 0.99). We found a significant reduction among diabetic patients who had at most 1 visit to the ED per year (RR = 0.97; 95% CI = 0.95, 0.99) and nonsignificant effects among more frequent users. Within low-enrolment regions, a 10-percentage point increase in enrolment in FMG practices at t - 1 led to an 18% decrease in the number of avoidable ED visits (RR = 0.82; 95% CI = 0.78, 0.87). The effect disappeared when the analyses were restricted to the high-enrolment regions (RR = 1.00; 95% CI = 0.92, 1.09). The design and implementation of the incentive to promote team-based practice may not have borne much influence on early adopters who may have been overrepresented by physicians from high-performing practices before the introduction of the reform.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".