BARRIERS AND FACILITATORS TO DEPRESCRIBING IN PRACTICE
Bibliographic record
Abstract
Identifying barriers and facilitators to deprescribing is a prerequisite for successful medication cessation. This study investigated barriers and facilitators to deprescribing in long-term care from the perspectives of patients, physicians, nurses, pharmacists and multidisciplinary teams. Semi-directed focus groups were conducted using nominal group technique with 56 key informants working or residing in long-term care in South Australia. Nineteen physicians, 12 nurses, 11 pharmacists, and 11 patients discussed the barriers and facilitators to deprescribing that they perceive. Thematic content analysis and ranking was performed by each group to generate a prioritized list of barriers and facilitators. Common themes were identified although priorities differed between focus groups. Barriers included evidence for deprescribing, poor communication, and fear of deterioration while ability to identify patient’s goals of care was an enabler. Awareness of barriers and facilitators can inform future research and development of tools to assist clinicians to deprescribe.
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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 | 0.016 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".