Evaluation of risk mitigation measures for people with substance use disorders to address the dual public health crises of COVID-19 and overdose in British Columbia: a mixed-method study protocol
Bibliographic record
Abstract
INTRODUCTION: The COVID-19 pandemic was preceded by an ongoing overdose crisis and linked to escalating drug overdose deaths in British Columbia (BC). At the outset of these dual public health emergencies, the BC government announced interim Risk Mitigation Guidance (RMG) that permitted prescribing medication alternatives to substances, including opioids, alcohol, stimulants and benzodiazepines, an intervention sometimes referred to as 'safe supply'. This protocol outlines the approach for a study of the implementation of RMG and its impacts on COVID-19 infection, drug-related and systemic harms, continuity of care for people with substance use disorder (SUD), as well as their behavioural, psychosocial and well-being outcomes. METHODS AND ANALYSIS: We conducted a parallel mixed-method study that involved both analysis of population-level administrative health data and primary data collection, including a 10-week longitudinal observational study (target n=200), a cross-sectional survey (target n=200) and qualitative interviews (target n=60). We implemented a participatory approach to this evaluation, partnering with people with lived or living expertise of drug use, and researchers and public health decision-makers across the province. Linked population-level administrative databases will analyse data from a cohort of BC residents with an indication of SUD between 1996 and 2020. We will execute high-dimensional propensity score matching and marginal structural modelling to construct a control group and to assess the impact of RMG dispensation receipt on a collaboratively determined set of primary and secondary outcomes. ETHICS AND DISSEMINATION: Study activities were developed to adhere to the Tri-Council Policy Statement: Ethical Conduct for Research Involving Humans, recommended COVID-19 research practices, and guided by the Truth and Reconciliation Commission's Calls to Action for public health, data governance and research ethics related to Indigenous people. Results will be disseminated incrementally, on an ongoing basis, through the consortium established for this study, then published in peer-reviewed journals.
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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 | 0.049 | 0.033 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".