Canadian government discourses on the overdose death crisis: limitations of a bio-evidenced approach
Bibliographic record
Abstract
Purpose Critical analyses of health policies and practices may appear to lack practicality during unprecedented times that demand immediate solutions. This paper aims to use critical social science theories to help improve essential service delivery during a public health crisis. Design/methodology/approach This study is based on qualitative content analysis of government and scholarly sources between 2008 and 2021 to identify strengths and gaps underlying the Canadian Federal Government’s evidence-based solutions to the opioid death crisis. Key questions examined are: What constitutes best-evidence practices underlying the Canadian Drugs and Substances Strategy?, Is biomedical evidence the only legitimate framework to substantiate feasible interventions? and Because the opioid death crisis affects disproportionately vulnerable populations, what is the potential merit of considering diverse knowledges and practices as valid forms of intervention despite lacking biomedical evidence bases? Findings While overdose reversing drugs, drug replacement approaches, biologically focused harm reduction options and pharmacological regulatory and surveillance initiatives help reduce premature opioid-related morbidity and mortality across provinces, this study’s findings demonstrate that these individualizing, biomedical magic bullets are temporary solutions, not comprehensive plans to solve a societal problem. This study’s theoretically informed analysis shows that the Canadian Federal Government responses detract attention from issues of social justice, social inequities and the biomedical dominance of health care as broader forces of the opioid death crisis. To address these analytical omissions, broader evidence-based solutions must build upon meaningful intraventions, the insiders’ perspectives or voices of the afflicted communities alongside meaningful interventions – going beyond distal, clinical-based and proximal, home-based interventions. Originality/value By highlighting the biomedical and social embeddings of the opioid death crisis, this study underscores structural conditions rather than individuals’ physical bodies as the catalysts for change. A deeper theoretical understanding of why certain issues exists, as they do and how they occur, can provide the basis for prediction of their (re)occurrence and for informing meaningful intervention efforts.
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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 | 0.116 | 0.172 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.057 | 0.076 |
| Scholarly communication | 0.026 | 0.015 |
| Open science | 0.009 | 0.018 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.006 | 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".