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Record W3144134235 · doi:10.1002/jppr.1713

Engaging general practice and patients with AusTAPER, a pharmacist facilitated web‐based deprescribing tool

2021· article· en· W3144134235 on OpenAlexaff
Lynne Parkinson, Parker Magin, Christopher Etherton‐Beer, Vasi Naganathan

Bibliographic record

VenueJournal of Pharmacy Practice and Research · 2021
Typearticle
Languageen
FieldHealth Professions
TopicPatient-Provider Communication in Healthcare
Canadian institutionsMcMaster University
FundersRACGP FoundationRoyal Australian College of General Practitioners
KeywordsDeprescribingMedicinePharmacistThematic analysisQualitative researchNursingGlobal Positioning SystemFamily medicinePharmacyPolypharmacyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Science and technology studies, Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.866
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.002
Open science0.0000.000
Research integrity0.0000.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.397
GPT teacher head0.557
Teacher spread0.160 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations14
Published2021
Admission routes1
Has abstractyes

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