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Record W3033188832 · doi:10.1177/2399202620922507

Influencers on deprescribing practice of primary healthcare providers in Nova Scotia: An examination using behavior change frameworks

2020· article· en· W3033188832 on OpenAlexafffundabout
Natalie Kennie‐Kaulbach, Rachel Cormier, Olga Kits, Emily Reeve, Anne Marie Whelan, Ruth Martin‐Misener, Fred Burge, Sarah Burgess, Jennifer E. Isenor

Bibliographic record

VenueMedicine Access Point of Care · 2020
Typearticle
Languageen
FieldMedicine
TopicPharmaceutical Practices and Patient Outcomes
Canadian institutionsUniversity of TorontoHorizon Health NetworkNova Scotia Health AuthorityDalhousie University
FundersDalhousie University
KeywordsDeprescribingInfluencer marketingNursingHealth careNova scotiaContext (archaeology)MedicinePsychologyPolypharmacyBusinessSociology

Abstract

fetched live from OpenAlex

Background: Deprescribing is a complex process requiring consideration of behavior change theory to improve implementation and uptake. Aim: The aim of this study was to describe the knowledge, attitudes, beliefs, and behaviors that influence deprescribing for primary healthcare providers (family physicians, nurse practitioners (NPs), and pharmacists) within Nova Scotia using the Theoretical Domains Framework version 2 (TDF(v2)) and the Behavior Change Wheel. Methods: Interviews and focus groups were completed with primary care providers (physicians, NPs, and pharmacists) in Nova Scotia, Canada. Coding was completed using the TDF(v2) to identify the key influencers. Subdomain themes were also identified for the main TDF(v2) domains and results were then linked to the Behavior Change Wheel—Capability, Opportunity, and Motivation components. Results: Participants identified key influencers for deprescribing including areas related to Opportunity, within TDF(v2) domain Social Influences, such as patients and other healthcare providers, as well as Physical barriers (TDF(v2) domain Environmental Context and Resources), such as lack of time and reimbursement. Conclusion: Our results suggest that a systematic approach to deprescribing in primary care should be supported by opportunities for patient and healthcare provider collaborations, as well as practice and system level enhancements to support sustainability of deprescribing practices.

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)0.000
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.132
Threshold uncertainty score0.674

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.319
GPT teacher head0.478
Teacher spread0.158 · 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

Citations29
Published2020
Admission routes3
Has abstractyes

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