The combined impacts of toxic drug use and the 2021 Heat Dome in Canada: A thematic analysis of online news media articles
Bibliographic record
Abstract
INTRODUCTION: During the summer of 2021, western Canada experienced a historic and deadly extreme heat event. Simultaneously, toxic drug use and overdoses related to high-risk use of opioids and polysubstance use continued to rise across the country. However, the combined impacts of these intersecting public health crises remain poorly understood as relevant data sources are limited in Canada. METHODS: We explored news media articles (n = 86, 3%) discussing toxic drug use, overdose-related deaths and the 2021 Heat Dome which were identified in a systematic review of Canadian online news media (e.g., newspaper articles, radio broadcasts) from five subscription news databases and an extensive grey literature search (n = 2909). The analyzed articles were published before, during and after the 2021 Heat Dome, and were qualitatively coded and thematically analyzed in NVivo to identify patterns of meaning across the dataset. RESULTS: Four main themes were identified within our media-based analysis: (I) the reported impact of toxic drug use on human thermoregulation and behavioural adaptation; (II) the reported demand of intersecting crises on the health system; (III) barriers and stigma reported to influence an individual's access to or use of heat mitigation behaviours and services; and (IV) the reported impact of extreme heat on the public health response to drug poisoning emergencies. CONCLUSION: With increasing temperature extremes and a rising tide of toxic drug use and overdoses, our findings illustrate that there is a need for further research to better understand the combined impacts when toxic drug use, overdose-related deaths, and extreme heat coincide.
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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 | 0.012 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.044 | 0.075 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.002 | 0.006 |
| 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".