Emotional and Physical Child Abuse in The Context of Natural Disasters: A Focus on Haiti
Bibliographic record
Abstract
OBJECTIVE: To investigate the social and living conditions of households in Haiti before and after the 2010 earthquake and to determine the prevalence of emotional and physical abuse of children aged 2 to 14 in households after the earthquake. METHODS: Nationally representative samples of Haitian households from the 2005/2006 and 2012 phases of the Demographic and Health Surveys were used. Descriptive data were summarized with frequencies and measures of central tendency. Chi-squared and independent t tests were used to compare pre-earthquake and post-earthquake data. Basic mapping was used to explore patterns of child abuse in relation to proximity to the epicenter. RESULTS: Comparison of pre-earthquake and post-earthquake data showed noteworthy improvements in the education attainment of the household head and possession of mobile phones after the earthquake. The prevalence of emotional, physical, and severe physical abuse in 2012 was estimated to be 78.5%, 77.0%, and 15.4%, respectively. Mapping revealed no conclusive patterns between the proximity of each region to the epicenter and the prevalence of the different forms of abuse. However, the prevalence of severe physical abuse was notably higher in settlement camps (25.0%) than it was in Haiti overall (15.4%). CONCLUSIONS: The high prevalence of child abuse in Haiti highlights an urgent need for interventions aimed at reducing occurrences of household child abuse.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".