Working under the 2021 Heat Dome: A Content Analysis of Occupational Impacts Mentioned in the Canadian Media
Bibliographic record
Abstract
Extreme heat events directly impact worker health and cause additional cascading and transitional workplace impacts. However, current investigations on these impacts often rely on specific datasets (e.g., compensation claims, hospitalizations). Thus, to continue to work towards preventing and mitigating the occupational risks posed by extreme heat events, this study aimed to explore the occupational impacts of the 2021 Heat Dome in Canada using a qualitative content analysis method on a news-based dataset. A systematized review of news articles published before, during, and after the 2021 Heat Dome was conducted on academic (n = 8) and news (n = 5) databases, along with targeted grey literature. Two researchers qualitatively coded the articles in NVivo for occupational impacts or references mentioned within the articles. Overall, 52 different occupations were identified as being impacted by the 2021 Heat Dome. Impacts were diverse and ranged from work cancellations or delays to work modifications and reports of heat-related illnesses. The 2021 Heat Dome impacted the health and safety of many occupational groups and provided new insights into the expanding impacts that extreme heat events can have on the Canadian workforce. With climate projections showing a growing trend of more hot days and intense heat waves in Canada, addressing these concerns should be a critical priority.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.038 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".