Understanding the behavioural determinants of opioid prescribing among family physicians: a qualitative study
Bibliographic record
Abstract
BACKGROUND: Longstanding variation in the views of family physicians (FPs) on the role of opioids seems to translate into widely varying prescribing rates. Improvement interventions are unlikely to achieve change if they do not understand and explicitly target the factors that determine physician prescribing behaviour. The aim of this work was to understand (1) the perspectives of FPs as it relates to opioid prescribing, and (2) the perceived barriers and enablers to guideline-adherent opioid prescribing and management of chronic non-cancer pain. METHODS: A qualitative study involving one-on-one, semi-structured interviews with a sample of FPs in Ontario, Canada. Interviews were analyzed using a directed content analysis informed by the Theoretical Domains Framework. A framework approach was used to explore interaction across behavioural determinants (factors influencing behaviour) as well as demographic sources of variation. The behaviour of interest for the current study was the prescribing of opioid medications (including initiation, renewal, and dose reduction) for patients with chronic, non-cancer pain. Associated issues in the overall management of such patients were also explored. RESULTS: Interviews were conducted with 22 FPs. Behavioural determinants interacted with one another to influence FPs prescribing behavior. The TDF domain Beliefs about Consequences played a central role in explaining physician prescribing behaviours as they related to the management of chronic non-cancer pain. Individual beliefs about prescribing consequences and patient behaviour interacted with prescriber beliefs about capabilities and perceptions of the FP's professional role to influence prescriber behaviour. Emotion and the environmental context influenced the impact of these determinants on opioid prescribing and the management of chronic non-cancer pain. CONCLUSIONS: FPs face a wide range of complex (and often interacting) challenges when prescribing opioid therapy to their patients. Solution-based strategies should target these determinants directly using evidence-based strategies that move beyond guideline dissemination and general education. Shared decision-making strategies and patient-facing decision aids are likely to decrease the tension experienced in challenging conversations.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".