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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".