The impact of environmental shocks due to climate change on intimate partner violence: A structural equation model of data from 156 countries
Bibliographic record
Abstract
The impact of climate change on human societies is now well recognised. However, little is known about how climate change alters health conditions over time. National level data around climate shocks and subsequent rates of intimate partner violence (IPV) could have relevance for resilience policy and programming. We hypothesise that climate shocks are associated with a higher national prevalence of IPV two years following a shock, and that this relationship persists for countries with different levels of economic development. We compiled national data for the prevalence of IPV from 363 nationally representative surveys from 1993 to 2019. These representative data from ever-partnered women defined IPV incidence as any past-year act of physical and/or sexual violence. We also compiled data from the Emergency Events Database (EM DAT) on the national frequency of eight climate shocks from 1920 to 2022 within 190 countries. Using exploratory factor analysis, we fit a three-factor latent variable composed of climate shock variables. We then fit a structural equation model from climate shocks (lagged by two years) and IPV incidence, controlling for (log) national gross domestic product (GDP). National data representing 156 countries suggest a significant relationship between IPV and a climate factor (Hydro-meteorological) composed of storms, landslides and floods (standardised estimate = 0·32; SE = 0·128; p = 0·012). GDP has a moderately large cross-sectional association with IPV (estimate = -0·529; SE = 0·047; p = 0·0001). Other climate shocks (Geological: earthquakes/volcanos; Atmospheric: wildfire/droughts/extreme temperature) had no measurable association with IPV. Model fit overall was satisfactory (RMSEA = 0·064 (95%CI: 0·044–0·084); CFI = 0·91; SRMR = 0·063). Climate shocks have a longitudinal association with IPV incidence in global population-based data. This suggests an urgent need to address the higher prevalence of IPV likely to come about through climate shocks due to climate change. Our analysis offers one way policy makers could track national progress using existing data.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".