Supporting deprescribing in long-term care: An approach using stakeholder engagement, behavioural science and implementation planning
Bibliographic record
Abstract
Approaches for optimizing medication use and enhancing medication experiences, including deprescribing, for older people living in long-term care homes are urgently needed. Through a multiphase initiative involving an environmental scan (2018) and two stakeholder forums (2019, 2020), we created a framework for developing and implementing sustainable deprescribing practices in this sector. Representatives from public advocacy, health care professionals, long-term care, pharmacy service providers, and regional health and public policy organizations in Ontario, Canada were consulted. We used behavioural science and implementation planning strategies to develop four target behaviours and 14 supporting actions; five of these actions were prioritized for further work. Throughout the phases, stakeholders committed to participation at various levels including ongoing implementation teams working to develop resources for the prioritized actions. A key element of success was attracting and sustaining engagement of a wide variety of relevant stakeholders from across the health system by leveraging best practices in stakeholder engagement. The approach used is described in detail so that it can be adapted and applied by others to plan large behaviour change initiatives.
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 | 0.109 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.013 | 0.012 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".