PP102 Developing A Contextually-Informed Deprescribing Intervention
Bibliographic record
Abstract
Introduction: Deprescribing – a process for reducing or stopping drugs when the balance of benefits and harms may no longer be in a person's interests – is a key aspect of managing multimorbidity and polypharmcacy in older people. Several deprescribing interventions have been developed (e.g. in Australia and Canada), although significant challenges for successful implementation remain. Through key stakeholder consultation in the care home setting in South West England, we take the initial steps to develop a context-informed deprescribing approach. Engaging stakeholders from the outset gains insight into acceptability, feasibility, and relevance of deprescribing interventions developed elsewhere informing co-production of an effective, implementable approach. Methods: Consultation workshops were held with two groups of stakeholders: (i) care home residents and their families; (ii) care home staff and health care professionals (general practitioners, medical specialists, pharmacists, nurses, allied health professionals). Focus groups were held with each group separately to understand perspectives on: deprescribing in general; contextual considerations; and, perspectives on deprescribing interventions developed in other countries. A combined focus group then considered components of a deprescribing intervention for care homes. Qualitative data were audio recorded, transcribed, and thematically coded. Results: Participants described the nature of local relationships, dynamics, structures, and resources, as important considerations in the development of a deprescribing approach in care homes. Perspectives and concerns around deprescribing among the stakeholder groups varied, although the importance of eliciting local stakeholder feedback in the early stages of developing a deprescribing intervention was a common thread. Conclusions: Early engagement and co-production are crucial in developing an approach to deprescribing in care homes. The combination of stakeholder involvement and qualitative research is important for developing an effective, contextually relevant intervention as the balance between interests can be incorporated into the approach. Leveraging the experience in other countries is a novel and valuable step.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.014 | 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".