“Death Is a Possibility for Those without Shelter”: A Thematic Analysis of News Coverage on Homelessness and the 2021 Heat Dome in Canada
Bibliographic record
Abstract
Among the most vulnerable to the health-harming effects of heat are people experiencing homelessness. However, during the 2021 Heat Dome, the deadliest extreme heat event (EHE) recorded in Canada to date, people experiencing homelessness represented the smallest proportion of decedents (n = 3, 0.5%)-despite the impacted region (British Columbia) having some of the highest rates of homelessness in the country. Thus, we sought to explore the 2021 Heat Dome as a media-based case study to identify potential actions or targeted strategies that were initiated by community support agencies, individuals and groups, and communicated in the news during this EHE that may have aided in the protection of this group or helped minimize the mortality impacts. Using media articles collated for a more extensive investigation into the effects of the 2021 Heat Dome (n = 2909), we identified a subset which included content on people experiencing homelessness in Canada (n = 274, 9%). These articles were thematically analysed using NVivo. Three main themes were identified: (i) public warnings issued during the 2021 Heat Dome directly addressed people experiencing homelessness, (ii) community support services explicitly targeting this population were activated during the heat event, and (iii) challenges and barriers faced by people experiencing homelessness during extreme heat were communicated. These findings suggest that mass-media messaging and dedicated on-the-ground initiatives led by various organizations explicitly initiated to support individuals experiencing homelessness during the 2021 Heat Dome may have assisted in limiting the harmful impacts of the heat on this community.
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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 | 0.007 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.009 | 0.016 |
| Science and technology studies | 0.012 | 0.007 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| 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".