Engaging general practice and patients with AusTAPER, a pharmacist facilitated web‐based deprescribing tool
Bibliographic record
Abstract
Abstract The objective of this study was to explore the Australian general practitioner (GP) and patient experience of AusTAPER, a pharmacist facilitated web‐based deprescribing tool, within a pilot implementation of the tool. This qualitative study of experiences of using AusTAPER in clinical practice used one‐on‐one interviews with patients (≥70 years, taking ≥5 medicines) and GPs. Thematic content analyses for patients and GPs were triangulated to synthesise findings. Nine patients and two GPs responded. Three main themes arose from the synthesised results: ‘engagement of GPs and patients’; ‘pharmacist as central’; and ‘patient outcomes’. AusTAPER prompted qualitative deprescribing and was acceptable to both GPs and patients. Patients appreciated medicines being monitored by pharmacists. There was evidence of synergy of GP and pharmacist opinion in facilitating patient understanding and shared decision‐making. These qualitative findings provide evidence that AusTAPER engaged GPs and patients and prompted judicious medicine review and deprescribing.
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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 | 0.021 |
| 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 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".