The changing landscape of pharmaceutical alternatives to the unregulated drug supply during COVID-19
Bibliographic record
Abstract
BACKGROUND: The dual COVID-19 and overdose emergencies amplified strain on healthcare systems tasked with responding to both. One downstream consequence of the pandemic in the USA and Canada was a surge in drug overdoses resulting from public health-restricted access to services and an increasingly toxic unregulated drug supply. This study aimed to describe changes implemented by programs prescribing pharmaceutical alternatives to the drug supply during the early stages of the COVID-19 pandemic. METHODS: An environmental scan used surveys and qualitative interviews with service providers across Canada to examine pharmaceutical alternative prescribing practices and programs before and during the pandemic. This study summarized the nature, frequency, and reasons for pandemic-driven service delivery changes using directed content analysis, counts, and thematic analysis. RESULTS: Eighty-two of the 103 participating sites reported 1193 unique changes in physical space (368), client protocols (347), program operations (342), ancillary services (127), and staffing (90). Four qualitative themes describing the reasons for these changes emerged, namely (1) decreasing risk of COVID-19 infection; (2) decreasing risk of overdose; (3) prioritizing acute care of COVID-19 patients; and (4) improving client access to treatment. CONCLUSIONS: While most changes were aimed at decreasing risk of COVID-19 infection, some were found to be at odds with the measures needed to combat the overdose crisis; others met dual objectives of decreased risk of both overdose and infection. Further research should examine which changes should be kept or reversed once COVID-19-related public health measures are lifted.
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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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".