“Breaking down in tears, soaked in sweat, and sick from the heat”: Media‐based composite narratives of first responders working during the 2021 Heat Dome
Bibliographic record
Abstract
BACKGROUND: During the summer of 2021, a deadly, unprecedented multiday Heat Dome engulfed western Canada. As a result of this extreme heat event (EHE), emergency dispatchers received an unparalleled increase in incoming 911 calls for ambulance, police, and fire (as first responders) services to attend to hundreds of heat-vulnerable community members succumbing to the heat. With 103 all-time heat records broken during this EHE and indoor temperatures of nearly 40°C, the first responders attending these calls faced extensive job demands and highly challenging operating conditions. Initial investigations have explored the health system-level impacts; however, little has been done to explore the impact on the first responders themselves. Therefore, this study aimed to improve our understanding of EHEs' impacts on the operational capabilities and health of first responders, specifically police, fire, ambulance, and dispatch services. METHODS: A systematized review and content analysis of media articles published on the 2021 Heat Dome in Canada was conducted (n = 2909), and four media-based composite narratives were developed highlighting police, fire, ambulance, and dispatch services. The Job Demands-Resources (JD-R) model was applied as a theoretical framework for occupational burnout. RESULTS: The media-based composite narratives highlighted that first responders faced record-breaking call volumes, increased mental-health-related claims, and exhaustive heat-related physiological stress. Using the JD-R model as a theoretical framework for occupational burnout, we identified three measures of stressful job demand: work overload (e.g., the surge in call volume, firefighters responding to medical emergencies), emotional demands (e.g., severe medical emergencies, sudden deaths, unresponsive patients, distraught family members), and physical demands (e.g., resuscitation in personal protective equipment, heat-related illness). CONCLUSION: The experiences described underscore the importance of supporting first responders during work in extreme heat conditions. These findings have important implications for addressing rising rates of burnout during and following public health crises, such as EHEs, a problem that is increasingly being recognized as a threat to the Canadian public healthcare system.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 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".