Barriers and enablers to opioid deprescription: A qualitative study
Bibliographic record
Abstract
BACKGROUND: Canada has the fourth highest per capita rate of opioid prescriptions in the world, contributing to the country's opioid crisis. Due to both their pain-relieving and euphoric properties, opioids can be highly addictive, leading to potential overdose and death. Deprescription is an endorsed and organized method of discontinuing a drug but very little is known about the barriers that Canadian physicians face when attempting to deprescribe opioids, particularly those who practice in rural areas (which have some of the highest rates of opioid users). METHODS: This was an explorative, qualitative study describing rural family doctors' experiences and practices regarding opioid deprescription in primary care. A convenience sample of family doctors who had experience working with patients taking opioid medications was recruited from the professional networks of study team members. After consenting to participate, data was collected using semi-structured telephone interviews and analyzed by researchers experienced in applying the Theoretical Domains Framework to assess barriers and enablers of behavior change. PRINCIPAL FINDINGS: 10 physicians participated in this study. Our analysis revealed four barriers and five enablers related to opioid deprescription in rural primary care. Barriers include a lack of knowledge and skills related to deprescribing, discomfort initiating deprescription, patient pressure to continue prescribing opioids, and a lack of foundational support required to deprescribe. Enablers include working with colleagues who share common views on overuse of opioids and deprescription; access to other healthcare providers, community-based resources, and clinical tools; using a systematic approach to deprescription; previous experience successfully deprescribing opioids; and practicing in a rural setting. CONCLUSIONS: Opioid dependence and over-prescription continue to be a problem for our health system. Deprescription is necessary but challenging for family physicians. Rural physicians are keenly aware of the importance of preserving the physician-patient therapeutic relationship and open and clear communication about opioid medications and deprescription but feel unprepared to manage this in the face of difficult issues surrounding deprescription. They also feel unprepared to deal with deprescription effectively without access to other resources, healthcare professionals, patient education materials and time. Rural physicians would benefit most from added foundational supports for deprescription.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.007 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".