Adaptation to heat stress: a qualitative study from Eastern India
Bibliographic record
Abstract
Abstract Heat stress adversely impacts a growing proportion of individuals in India. The heat-related lived experiences of Indians in smaller towns and villages are largely unknown. We conducted seven structured focus group discussions in the town of Dalkhola, West Bengal, India; with 5–10 participants in each group. All conversations were digitally audio recorded, transcribed into Bengali, and then translated to English. Two researchers separately performed a thematic analysis of the transcripts to identify common themes pertaining to the ‘effects of heat’ and ‘coping strategies’ used by participants. A total of 56 (mean age 48.9 ± 17.6; female 61%; Scheduled Tribe 9%) individuals participated. There was wide variation in individual experiences of heat, with some people preferring to work in the winter while others preferred the summer. Housing characteristics, nature of work, gender and access to water and green spaces heavily influenced an individual’s vulnerability to heat stress. Trees were seen as the primary coping strategy for heat stress (regardless of vulnerability), though many participants noted a loss of tree cover in their vicinity. Cool drinking water from public taps and electric fans (particularly table fans) were other preferred coping mechanisms. Many participants did not have adequate access to cool drinking water or electric fans, leading to increased adverse experiences from heat. Based on participant input, several action items were identified for municipal and state/central governments, schools, and private organizations. Individuals affected by heat have a clear preference for nature-based solutions. This is in contrast with the current design of most heat action plans in India, which put more emphasis on infrastructure, information dissemination and behavioral solutions. Various agencies (governments, schools, private organizations) seeking to adapt to increasing heat stress need to better integrate citizen perspectives into their heat action plans.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".