Heat is a danger to my health even though I said I am used to it”: Qualitative insights of workplace heat among community health workers and health promoters in Kenya [data]
Bibliographic record
Abstract
Climate change is one of the most prominent environmental and health challenges of the 21st century. Variations in extreme temperature and weather events intensify occupational heat exposure and place workers at increasing risk of heat-related illness (HRIs) and injury. Healthcare workers, especially those in resource-limited, community-based, or mobile settings, face significant occupational risks from rising temperatures, yet these challenges remain largely overlooked and insufficiently studied. This qualitative study, based on semi-structured face-to-face interviews, explores the experiences of Community Health Workers (CHWs) and Community Health Promoters (CHPs) in Kenya, examining how extreme heat affects their personal health, livelihoods, and the delivery of community-based health services. We conducted 41 in-depth interviews with CHWs and CHPs (Mombasa County, n=19; Tana River County, n=22). Data was managed using NVivo 14 and analysed drawing on tenets of reflexive thematic analysis. We identified a pattern of intersecting vulnerabilities shaped by experiences of economic inequality, work conditions and pressures, HRIs, and challenges of accessing healthcare, effects of changing weather patterns on community health work and livelihoods, and gendered experiences of extreme weather and work challenges. Our findings show that these domains are not discrete but reinforcing, with overlapping effects that not only shape the daily experiences of CHWs and CHPs but also constrain their resilience and the effectiveness of community health service delivery. Our findings highlight the urgent need for climate-resilient health systems that not only improve the working conditions or protect CHWs and CHPs from extreme heat but also address the structural inequalities, such as economic disparities and the challenges of gendered burdens, that heighten their vulnerability.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.000 | 0.008 |
| Open science | 0.009 | 0.016 |
| Research integrity | 0.001 | 0.007 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".