Influencers on deprescribing practice of primary healthcare providers in Nova Scotia: An examination using behavior change frameworks
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".