Reducing extreme heat impacts on health in pregnant women and infants: a community based intervention in Kilifi, Kenya
Bibliographic record
Abstract
High ambient temperatures affect maternal and newborn health outcomes and wellbeing. The Climate Heat and Maternal and Neonatal Health in Africa (CHAMNHA) consortium conducted formative qualitative research in rural Kilifi, Kenya, to examine perceptions of heat risks among women, household members, and community stakeholders. An intervention was co-designed together with community members. This paper presents the development, implementation, and evaluation of a behaviour-change intervention aimed at reducing the burden of heat on maternal and newborn health. The intervention used Digital Audio-Visual (DAV) storytelling (encompassing short videos and a set of photographs) and facilitated group discussions. Intervention groups included pregnant and postpartum women (n = 10), mothers-in-law (n = 10), male spouses (n = 10), and community influencers (n = 40). Researchers and local community health volunteers supported pregnant and postpartum women and their household networks weekly for 4 months. At month five, a structured interview, originally administered at baseline, was repeated to evaluate understandings of heat risks and changes in behaviour (reducing exposure to heat by changing daily schedules, reducing heavy workload, and increasing spousal support). Pregnant and postpartum women reported a better understanding of the effects of heat on their health and the newborn, including the importance of staying hydrated, breastfeeding frequently, and avoiding heavy clothing for newborns. They also reported an increase in mothers-in-law and male spouses assisting with household chores and disseminating heat-health messaging to families. However, women noted that male spouses who supported them with chores sometimes reported being stigmatized by their peers. Community approaches to support pregnant and postpartum women during heat periods are feasible, and key community influencers can be trained to include heat-health messaging in their daily routines. Additional research is needed to examine whether repeated training is required to ensure sustainability. Future heat interventions focusing on maternal and neonatal health should consider factors such as employment, age, and depth of support networks.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".