Using Community Engagement to Initiate Conversations About Medication Management and Deprescribing in Primary Care
Bibliographic record
Abstract
Polypharmacy, or the simultaneous use of multiple medications, represents a significant public health challenge—particularly among older adults, who are more likely to experience negative clinical outcomes attributable to adverse reactions to or interactions between their medications (Canadian Institute for Health Information, 2013). Improved medication management on the part of both patients and health care providers (HCPs) is needed to address the issues and consequences associated with polypharmacy, but conversations between patients and their HCPs about options for medication changes remain the exception. In a rural community near Ottawa, Ontario, a community-based participatory research (CBPR) approach aimed to support improved public awareness of and participation in medication management and deprescribing through educational events aimed at older adults. This paper describes the processes researchers used in collaboration with community members to discuss and address medication management in a locally relevant manner, details the results of these processes, and suggests how similar approaches may be employed to empower patients and communities to address issues of personal health care.
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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 | 0.022 | 0.033 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".