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Record W2724782547 · doi:10.1093/geroni/igx004.2473

SHARED DECISION MAKING AND PATIENT-CENTERED DEPRESCRIBING

2017· article· en· W2724782547 on OpenAlexaboutno aff
Emily Reeve

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsnot available
Fundersnot available
KeywordsDeprescribingMedicineOlder peopleHealth careNursingFamily medicinePolypharmacyGerontology

Abstract

fetched live from OpenAlex

Older adult (or caregiver) resistance/refusal is often cited by prescribers as a barrier to deprescribing. Shared decision making is advocated not only because it is ethically appropriate, but also because it can prevent waste of resources and improve health outcomes. This session will present a portfolio of research into how older adults and caregivers feel about deprescribing and discuss strategies on how this knowledge can be translated into practice. Research conducted using the Patients’ Attitudes Towards Deprescribing (PATD) questionnaire in hospitals, aged care facilities and the community in Australia, Canada and Italy found that between 80 and 90% of older adults and caregivers of older adults are willing to have a medication deprescribed if their doctor said it was possible. Qualitative and quantitative studies have found common barriers and facilitators to deprescribing including patient belief in the appropriateness of the medication, the burden of medication taking and fear/concerns surrounding withdrawal.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.039
metaresearch head score (Gemma)0.062
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.039
Threshold uncertainty score0.209

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.062
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.001
Science and technology studies0.0050.012
Scholarly communication0.0070.006
Open science0.0020.012
Research integrity0.0030.006
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.182
GPT teacher head0.435
Teacher spread0.254 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2017
Admission routes1
Has abstractyes

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