Did the Implementation of Team-Based Primary Care Models in Ontario and Quebec, Canada, Impact Appropriate Prescribing?
Bibliographic record
Abstract
Context: Many older adults take multiple medications prescribed by a variety of providers, which leads to concerns about medication management, appropriateness, and adverse drug events. Interdisciplinary, team-based primary care models can improve coordination of health care services, which could translate to improved medication management and related outcomes. Objective: Evaluate the impact of interdisciplinary team-based primary care models implemented in two Canadian provinces — Ontario and Quebec — on outcomes related to medication use. Study Design and Analysis: Retrospective cohort analysis of population-level administrative health data. We used difference-in-differences analysis to compare older adults rostered to team-based primary care models, to older adults not rostered to team-based models. Dataset: Data housed at ICES in Ontario and the Institut national d’excellence en sante et services sociaux (INESSS) in Quebec. We focused on fiscal years 1999/00 to 2017/18. Population Studied: Eligible patients were between 66 and 104 years of age. We matched (1-to-1 propensity score matching without replacement) an exposure group of older adults who were rostered to a physician affiliated with a team-based primary care model to a comparison group of older adults rostered to non-team family physicians. Intervention: Quebec’s Family Medicine Groups (implemented in 2002) and Ontario’s Family Health Teams (implemented in 2005). Outcome Measures: Any adverse drug event resulting in hospitalization, polypharmacy (5+ medication classes), and any potentially inappropriate prescription (adapted from Beer’s and STOPP/START criteria). Results: Matched cohorts included 429,104 older adults in Ontario and 310,198 in Quebec. In the year before they rostered, 53% and 40% of older adults had a potentially inappropriate prescription in Ontario and Quebec, respectively. In both provinces, 1% had an adverse drug event. Quebec’s Family Medicine Groups were more likely to experience an adverse drug event (RR = 1.14; 95% CI: 1.10 - 1.17). We found no other differences between the exposure and comparison groups. Conclusions: The implementation of team-based primary care models in Ontario and Quebec was not associated with a variety of outcomes related to medication management. These results point to a need for further investigation of the composition and functioning of primary care teams to determine how they can support older adults with complex health needs
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".