Shaping future deprescribing priorities: outcomes of a World Café stakeholder workshop
Bibliographic record
Abstract
INTRODUCTION: Medicine-related harm associated with polypharmacy is a pertinent global health challenge. Deprescribing (reducing or stopping) medicines that cause more potential harm than benefit could mitigate the risk of medicine-related harm. However, the existing deprescribing research-to-practice gap threatens the long-term sustainability and scalability of deprescribing efforts. RESEARCH DESIGN AND METHODS: To address this, key stakeholders including healthcare practitioners, academics, policymakers and representatives of peak professional organizations, gathered at a World Café workshop to reflect on progress achieved in the deprescribing research and practice landscape while exploring the top future priorities for deprescribing. RESULTS: Thirty participants agreed on three top priorities: improving the clinical management of deprescribing; engaging consumers and gaining their perspectives; and raising awareness to enhance communication. Emerging themes and related barriers and catalysts were derived and mapped to a socio-ecological model offering a bird-eye's view of these factors on an individual, interpersonal, organizational, and societal level. CONCLUSIONS: Our World Cafe' highlights opportunities for future deprescribing research and practice. To promote the uptake of deprescribing in practice, catalysts could include leveraging new technology, promoting deprescribing via social media and optimizing workforce staff and knowledge. Ultimately, this knowledge may motivate deprescribing efforts and bridge the research-to-practice gap.
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 | 0.074 | 0.070 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.017 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".