The impact of climate shocks exposure to depressive and suicidal ideations among female population in Kilifi rural areas, Kenya
Bibliographic record
Abstract
BACKGROUND: Few African studies have established links between climate shocks and mental health outcomes. This study examines the impact of climate change-related shocks on depressive symptoms and suicidal thoughts in a group of 14,801 female participants dependent on the informal agricultural sector. METHODS: Women living in informal settlements without running water or flushing toilets were classified as the treatment group, while rural women with basic amenities served as the control group. We applied a two-stage least-squares model to assess the effects of climate shocks-reduced rainfall, heat waves, and drought-on depression and suicidal ideation. FINDINGS: Climate shocks contribute towards a 10.8% [95% CI: 2.3%-17.7%] increase in depressive symptoms in the women from the informal settlement group versus the women from rural households. These increases in depressive symptoms have significant negative spillover effects on suicidal ideation in the woman living in informal settlements. Less rain was associated with 28.7% [95% CI: 22.5%-34.5%] higher suicidal ideation in the woman living in informal settlements. Heat waves increased suicidal ideation by 14.9% [95% CI: 7.6%-20.7%]. Drought caused a 36.7% [95% CI: 29.4%-41.1%] increase in suicidal ideation. The accumulative effects of climate change shocks and high food prices increased suicidal ideation by 48.3% [95% CI: 35.2%-54.9%]. INTERPRETATION: Climate change shocks worsen depression and consequently drive suicidal thoughts in women from informal settlements with varying intensity. Kenyan policymakers may need to prioritize the provision of mental health services in the aftermath of climate change-related shocks. FUNDING: This study was supported by the Canadian philanthropic foundation called the Waverley House. This funding is used to support all research projects of the Brain and Mind Institute, Aga Khan University.
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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 | 0.000 | 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".