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Record W4394783921 · doi:10.1177/17151635241240737

Strategies to guide the successful implementation of deprescribing in community practice: Lessons learned from the front line

2024· article· en· W4394783921 on OpenAlexafffundvenueabout
Justin P. Turner, Kelda Newport, Aisling M. McEvoy, Tara P. Smith, Cara Tannenbaum, Deborah Kelly

Bibliographic record

VenueCanadian Pharmacists Journal / Revue des Pharmaciens du Canada · 2024
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversité de MontréalMemorial University of Newfoundland
FundersFonds de Recherche du Québec - SantéMemorial University of NewfoundlandMitacs
KeywordsFront lineDeprescribingFront (military)Line (geometry)Front coverPsychologyMedicineEngineeringHistoryPolypharmacyPharmacologyMechanical engineering

Abstract

fetched live from OpenAlex

Background: Sustainable implementation of new professional services into clinical practice can be difficult. In 2019, a population-wide initiative called SaferMedsNL was implemented across the province of Newfoundland and Labrador (NL), to promote appropriate medication use. Two evidence-based interventions were adapted to the context of NL to promote deprescribing of proton pump inhibitors and sedatives. The objective of this study was to identify and prioritize which actions supported the implementation of deprescribing in community practice for pharmacists, physicians and nurse practitioners across the province. Methods: Community pharmacists, physicians and nurse practitioners were invited to participate in virtual focus groups. Nominal Group Technique was used to elicit responses to the question: “What actions support the implementation of deprescribing into the daily workflow of your practice?” Participants prioritized actions within each group while thematic analysis permitted comparison across groups. Results: Five focus groups were held in fall 2020 involving pharmacists ( n = 11), physicians ( n = 7) and nurse practitioners ( n = 4). Participants worked in rural ( n = 10) and urban ( n = 12) settings. The different groups agreed on what the top 5 actions were, with the top 5 receiving 68% of the scores: (1) providing patient education, (2) allocating time and resources, (3) building interprofessional collaboration and communication, (4) fostering patient relationships and (5) aligning with public awareness strategies. Conclusion: Pharmacists, physicians and nurse practitioners identified similar actions that supported implementing evidence-based deprescribing into routine clinical practice. Sharing these strategies may help others embed deprescribing into daily practice and assist the uptake of medication appropriateness initiatives by front-line providers. Can Pharm J (Ott) 2024;157:xx-xx.

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)0.027
metaresearch head score (Gemma)0.036
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.027
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.036
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0070.006
Scholarly communication0.0090.008
Open science0.0050.011
Research integrity0.0050.007
Insufficient payload (model declined to judge)0.0040.001

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 head0.218
GPT teacher head0.461
Teacher spread0.244 · 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 designQualitative
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

Citations9
Published2024
Admission routes4
Has abstractyes

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