“I’m almost opioid-a-phobic”: family medicine residents’ perceptions of enhancing opioid analgesic and agonist treatment training in a Canadian setting
Bibliographic record
Abstract
PURPOSE: As deaths from the illicit drug poisoning crisis continue to rise in Canada, increasing the number of healthcare professionals qualified to effectively prescribe opioids could be beneficial. The willingness of family medicine residents to undertake structured training in prescribing opioids for Opioid Agonist Treatment (OAT) and pain management have not been well described. MATERIALS AND METHODS: = 20) in British Columbia, Canada, were asked about their experience with and willingness to enrol in OAT training. Informed by the Consolidated Framework for Implementation Research, data were analysed thematically using NVivo software. RESULTS: Four themes were identified: (1) challenges to training implementation, (2) feelings and attitudes on prescribing practices, (3) helpful learning spaces and places of substance use training, and (4) recommendations for implementing training. Preparedness, exposure, and supportive learning environments for substance use education increased willingness to pursue OAT accreditation, while ineffective learning experiences, mixed feelings about opioid prescribing, and lack of protected time were the most common reasons for unwillingness. CONCLUSIONS: Protected time and a range of clinical experiences appear to facilitate residents' willingness to complete OAT and opioid training. Implementation strategies to enhance the uptake of OAT accreditation in family medicine residency must be prioritised.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".