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Record W4411223612 · doi:10.1111/bcpt.70060

Barriers and Facilitators to Pharmacist‐Led Deprescribing of Antihypertensives in Long‐Term Care: A Survey‐Based Study

2025· article· en· W4411223612 on OpenAlexafffundabout
Ana Vucenovic, Roni Kraut, Donna Manca, Cheryl A Sadowski

Bibliographic record

VenueBasic & Clinical Pharmacology & Toxicology · 2025
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of Alberta
FundersFaculty of Pharmacy and Pharmaceutical Sciences, University of AlbertaUniversity of Alberta
KeywordsDeprescribingPharmacistMedicineTerm (time)PolypharmacyLong-term carePharmaceutical careNursingFamily medicineIntensive care medicinePharmacy

Abstract

fetched live from OpenAlex

Pharmacist-led deprescribing is an approach of addressing polypharmacy in the long-term care (LTC) setting. However, the sustainability of this practice has not been widely studied. This study describes facilitators and barriers to pharmacist-led deprescribing from pre- and post-surveys completed as part of a randomized controlled antihypertensive deprescribing trial in Alberta, Canada. The surveys, targeting facilitators and barriers to deprescribing, were developed based on current evidence and designed through an iterative process with pharmacist input and consist of open- and closed-ended questions. Nine pharmacists completed both surveys (seven female; five have been a pharmacist ≥10 years; and eight had their Additional Prescribing Authority [authority to prescribe to full scope of practice]). The key facilitators were (1) pharmacist confidence and attitude toward deprescribing, (2) sufficient patient data, and (3) minimal tools and education required. The key barriers included (1) pharmacist perception of not being the primary decision maker on prescribing decisions, (2) insufficient support from residents' families and physicians, and (3) additional time required. These facilitators and barriers were all identified pre-deprescribing, and confirmed, and more evident post-deprescribing. These barriers will make it challenging for pharmacists to incorporate deprescribing antihypertensives into routine care.

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)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.020
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.148
GPT teacher head0.501
Teacher spread0.354 · 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 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

Citations6
Published2025
Admission routes3
Has abstractyes

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