The impact of team-based primary care on medication-related outcomes in older adults: A comparative analysis of two Canadian provinces
Bibliographic record
Abstract
Objective: To evaluate if access to team-based primary care is related to medication management outcomes for older adults. Methods: We completed two retrospective cohort studies using administrative health data for older adults (66+) in Ontario (n = 428,852) and Québec (n = 310,198) who were rostered with a family physician (FP) between the 2001/02 and 2017/18 fiscal years. We generated matched comparison groups of older adults rostered to an FP practicing in a team-based model, and older adults rostered to an FP in a non-team model. We compared the following outcomes between these groups: any adverse drug reactions (ADRs), any potentially inappropriate prescription (PIP), and polypharmacy. Average treatment effects of access to team-based care were estimated using a difference-in-differences estimator. Results: The risk of an ADR was 22 % higher (RR = 1.22, 95 % CI = 1.18, 1.26) for older adults rostered to a team-based FP in Québec and 6 % lower (RR = 0.943, 95 % CI = 0.907, 0.978) in Ontario. However, absolute risk differences were less than 0.5 %. Differences in the risk of polypharmacy were small in Québec (RR = 1.005, 95 % CI = 1.001, 1.009) and Ontario (RR = 1.004, 95 % CI = 1.001, 1.007) and had absolute risk differences of less than 1 % in both provinces. Effects on PIP were not statistically or clinically significant in adjusted models. Interpretation: We did not find evidence that access to team-based primary care in Ontario or Québec meaningfully improved medication management outcomes for older adults.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.003 | 0.009 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 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".