Primary care physician characteristics associated with becoming opioid agonist treatment prescribers in British Columbia: a retrospective case-control study
Bibliographic record
Abstract
BACKGROUND: Opioid agonist treatment (OAT) is the gold standard of care for patients living with opioid use disorder. Since 2016, efforts to expand OAT access have focused on primary care physicians. This study aimed to understand how OAT-prescribing-naïve primary care physicians who began prescribing OAT differed from their peers who did not. METHODS: We used administrative health data to identify all patients eligible for OAT initiation between 1 January 2016 and 31 December 2019. We matched primary care visits that resulted in an OAT dispensation with visits that did not. We conducted logistic regression with generalized estimating equations to identify physician demographics and practice characteristics associated with becoming an OAT prescriber. RESULTS: Of the 4253 primary care physicians who were OAT-prescribing-naïve before 2016, 2183 (51.3%) began prescribing OAT. Physicians who practiced in rural settings (aOR = 1.78, 95% CI: 1.32, 2.40) or saw fewer than 16 patients a day (aOR = 1.46, 95% CI: 1.21, 1.75) were more likely to become OAT prescribers. The likelihood of becoming an OAT prescriber declined with the proportion of visits delivered out of office (aOR = 0.20, 95% CI: 0.16, 0.26) and years since graduation (e.g., physicians who graduated between 2000-2009 were 20% less likely to initiate (aOR = 0.80, 95% CI: 0.64, 0.99) compared with peers who graduated since 2010). CONCLUSIONS: Physicians who saw fewer patients and worked across fewer settings were more likely to become OAT prescribers. However, physicians in rural settings are stepping in to address unmet demand despite resource and time constraints. Understanding the differences between physicians who become OAT prescribers and peers who do not is critical to effectively target interventions to improve OAT access in the future.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".