Influence of physician networks on the implementation of pharmaceutical alternatives to a toxic drug supply in British Columbia
Bibliographic record
Abstract
BACKGROUND: Characterizing the diffusion of adopted changes in policy and clinical practice can inform enhanced implementation strategies to ensure prompt uptake in public health emergencies and other rapidly evolving disease areas. A novel guidance document was introduced at the onset of the COVID-19 pandemic in British Columbia (BC), Canada, which supported clinicians to prescribe opioids, stimulants, and benzodiazepines. We aimed to determine the extent to which uptake and discontinuation of an initial attempt at a prescribed safer supply (PSS) program were influenced through networks of prescribers. METHODS: We executed a retrospective population-based study using linked health administrative data that captured all clinicians who prescribed to at least one client with a substance use disorder from March 27, 2020, to August 31, 2021. Our main exposure was the prescribing patterns of an individuals' peers, defined as the proportion of a prescribers' professional network (based on shared clients), which had previously prescribed PSS, updated monthly. The primary outcome measured whether a clinician had prescribed their initial PSS prescription during a given calendar month. The secondary outcome was the discontinuation of PSS prescribing, defined as an absence for PSS prescriptions for at least 3 months. We estimated logistic regression models using generalized estimated equations on monthly repeated measurements to determine and characterize the extent to which peer networks influenced the initiation and discontinuation of PSS prescribing, controlling for network, clinician, and caseload characteristics. Innovators were defined as individuals initiating PSS prior to May 2020, and early adopters were individuals initiating PSS after. RESULTS: Among 14,137 prescribers treating clients with substance use disorder, there were 228 innovators of prescribed safer supply and 1062 early adopters through the end of study follow-up, but 653 (50.6%) were no longer prescribing by August 2021. Prescribers with over 20% of peers whom had adopted PSS had a nearly fourfold higher adjusted odds of PSS prescribing themselves (aOR: 3.79, 95% CI: (3.15, 4.56)), compared to those with no connected safer supply prescribers. CONCLUSIONS: The uptake of PSS in BC was highly dependent on the behavior of prescribers' peer networks. Future implementation strategies to support PSS or other policies would benefit from leveraging networks of prescribers.
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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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".