Improving opioid safety practices in primary care: protocol for the development and evaluation of a multifaceted, theory-informed pilot intervention for healthcare providers
Bibliographic record
Abstract
INTRODUCTION: In North America, drug overdose deaths are reaching unprecedented levels, largely driven by increasing prescription opioid-related deaths. Despite the development of several opioid guidelines, prescribing behaviours still contribute to poor patient outcomes and societal harm. Factors at the provider and system level may hinder or facilitate the application of evidence-based guidelines; interventions designed to address such factors are needed. METHODS AND ANALYSIS: Using implementation science and behaviour change theory, we have planned the development and evaluation of a comprehensive Opioid Self-Assessment Package, designed to increase adherence to the Canadian Opioid Guideline among family physicians. The intervention uses practical educational and self-assessment tools to provide prescribers with feedback on their current knowledge and practices, and resources to improve their practice. The evaluation approach uses a pretest and post-test design and includes both quantitative and qualitative methods at baseline and 6 months. We will recruit a purposive sample of approximately 10 family physicians in Ontario from diverse practice settings, who currently treat patients with long-term opioid therapy for chronic pain. Quantitative data will be analysed using basic descriptive statistics, and qualitative data will be analysed using the Framework Method. ETHICS AND DISSEMINATION: The University Health Network Research Ethics Board approved this study. Dissemination plan includes publications, conference presentations and brief stakeholder reports. This evidence-informed, theory-driven intervention has implications for national application of opioid quality improvement tools in primary care settings. We are engaging experts and end users in advisory and stakeholder roles throughout our project to increase its national relevance, application and sustainability. The performance measures could be used as the basis for health system quality improvement indicators to monitor opioid prescribing. Additionally, the methods and approach used in this study could be adapted for other opioid guidelines, or applied to other areas of preventive healthcare and clinical guideline implementation processes.
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 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 | 0.065 | 0.051 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.083 | 0.016 |
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".