What factors are important for deprescribing in Australian long-term care facilities? Perspectives of residents and health professionals
Bibliographic record
Abstract
OBJECTIVES: Polypharmacy and multimorbidity are common in long-term care facilities (LTCFs). Reducing polypharmacy may reduce adverse events and maintain quality of life. Deprescribing refers to reducing medications after consideration of therapeutic goals, benefits and risks, and medical ethics. The objective was to use nominal group technique (NGT) to generate then rank factors that general medical practitioners (GPs), nurses, pharmacists and residents or their representatives perceive are most important when deciding whether or not to deprescribe medications. DESIGN: Qualitative research using NGT. SETTING: Participants were invited if they worked with, or resided in LTCFs across metropolitan and regional South Australia. PARTICIPANTS: 11 residents/representatives, 19 GPs, 12 nurses and 14 pharmacists participated across six separate groups. METHODS: Individual groups of GPs, nurses, pharmacists and residents/representatives were convened. Using NGT each group ranked factors perceived to be most important when deciding whether or not to deprescribe. Then, using NGT, the prioritised factors from individual groups were discussed and prioritised by a multidisciplinary metropolitan and regional group comprised of resident representatives, GPs, nurses and pharmacists. RESULTS: No two groups had the same priorities. GPs ranked 'evidence for deprescribing' and 'communication with family/resident' as most important factors. Nurses ranked 'GP receptivity to deprescribing' and 'nurses ability to advocate for residents' as most important. Pharmacists ranked 'clinical appropriateness of therapy' and 'identifying residents' goals of care' as most important. Residents ranked 'wellbeing of the resident' and 'continuity of nursing staff' as most important. The multidisciplinary groups ranked 'adequacy of medical and medication history' and 'identifying residents' goals of care' as most important. CONCLUSIONS: While each group prioritised different factors, common and contrasting factors emerged. Future deprescribing interventions need to consider the similarities and differences within the range of factors prioritised by residents and health professionals.
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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 | 0.009 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".