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Record W3118996528 · doi:10.1177/0193945920982599

Educating Homecare Nurses about Deprescribing of Medications to Manage Polypharmacy for Older Adults

2021· article· en· W3118996528 on OpenAlexafffund
Winnie Sun, Farah Tahsin, Jennifer Abbass‐Dick, Caroline Barakat, Justin P. Turner, Dale Wilson, Cheryl Reid‐Haughian, Bahar Ashtarieh

Bibliographic record

VenueWestern Journal of Nursing Research · 2021
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsCARE CanadaUniversité de MontréalOntario Tech University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDeprescribingPolypharmacyLikert scaleMedicineIntervention (counseling)Thematic analysisNursingScale (ratio)Qualitative researchPsychology

Abstract

fetched live from OpenAlex

The purpose of this study is to evaluate the acceptability, appropriateness, and effectiveness of educational intervention with homecare nurses about deprescribing of medications among older adults. An evaluation research study was conducted using survey design to evaluate deprescribing education with a total sample of 45 homecare nurses from three homecare organizations. Post-training evaluation data were evaluated using Likert scale and open-ended questions were analyzed using descriptive statistical analyses and qualitative thematic analysis. Post-intervention questionnaire responses provided descriptions about homecare nurses' perspectives related to deprescribing education, as well as the effectiveness of training in addressing their knowledge gaps. The pilot-testing of deprescribing learning modules and educational training revealed acceptability and suitability for future scale-up to expand its future reach and adoption by other homecare organizations. This study provided important implications into the barriers that impact the effectiveness of deprescribing education, and facilitators that support the future refinement of learning modules.

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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.756
Threshold uncertainty score0.349

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.269
GPT teacher head0.570
Teacher spread0.301 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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 routes2
Has abstractyes

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