Strategies to promote public engagement around deprescribing
Bibliographic record
Abstract
Many seniors remain unaware that certain medications may be harmful, despite high rates of polypharmacy and inappropriate medication use among community-dwelling older adults. Patient education is an effective method for reducing the use of inappropriate medications. Increasing public awareness and engagement is essential for promoting shared decision-making to deprescribe. The Canadian Deprescribing Network was created to address the lack of a systematic pan-Canadian initiative to implement deprescribing among older Canadians. The Canadian Deprescribing Network deliberately included patient advocates in its organization from the outset, in order to ensure a key strategic focus on public awareness and education. In this paper, we present the processes and activities rolled out by the Canadian Deprescribing Network as a blueprint model for engaging the public on deprescribing. Embedded within the structure of the network, the subcommittee on public awareness and engagement implements an action plan that includes needs assessments, population surveys, focus groups, deprescribing fairs, national stakeholders' meetings, public lectures and monthly exchanges with community champions and seniors' organizations. Educational materials and online media have been developed based on the answers to the questions: what information do seniors need about deprescribing? who should this information be delivered to? who needs to deliver the message? and how should seniors be engaged in deprescribing? In conjunction with seniors' organizations, members of the Network have iteratively refined key deprescribing messages, disseminated information about deprescribing, engaged the press and created a grass roots-driven public awareness and education campaign across Canada. Over 3000 seniors and seniors' organizations are involved, with over 25,000 educational tools being distributed across the country.
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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 | 0.043 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.017 | 0.008 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.004 | 0.025 |
| Research integrity | 0.007 | 0.011 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".