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Record W2293628358 · doi:10.1136/bmjopen-2015-009781

What factors are important for deprescribing in Australian long-term care facilities? Perspectives of residents and health professionals

2016· article· en· W2293628358 on OpenAlex
Justin P. Turner, Susan Edwards, Melinda Stanners, Sepehr Shakib, J. Simon Bell

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueBMJ Open · 2016
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
FundersAustralasian Society of Clinical and Experimental Pharmacologists and ToxicologistsCanadian Geriatrics SocietyAmerican Political Science Association
KeywordsDeprescribingPolypharmacyMedicineNursingFamily medicineMultidisciplinary approachMetropolitan areaGeriatricsLong-term careHealth care

Abstract

fetched live from OpenAlex

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.

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.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.067
Threshold uncertainty score0.255

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.396
GPT teacher head0.555
Teacher spread0.159 · 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