Pathways to suicide prevention: a population-based cohort study of the mediating role of Bolsa Família in psychosocial stressors in the 100 million Brazilian cohort
Bibliographic record
Abstract
Introduction: Suicide is a major public health concern in Brazil, and its risk is heightened by psychosocial stressors disproportionately affecting socioeconomically vulnerable groups. This study aimed to investigate how the cash transfer Bolsa Família Programme (BFP) mediates the relationship between psychosocial stressors and suicide risk among low-income individuals in Brazil. Methods: We analysed data from the 100 Million Brazilian Cohort (2008-2015), which includes individuals registered in the Cadastro Único (national registry of low-income families) linked to hospitalisation and mortality records through deterministic and probabilistic methods. Psychosocial stressors were defined as: (1) violence (hospitalisation for interpersonal violence); (2) impulsivity (unintentional injury hospitalisations); and (3) psychiatric hospitalisation (International Classification of Diseases, 10th Revision (ICD-10):F00-F99). Suicide mortality (ICD-10 X60-X84) was the outcome. Analyses included individuals aged ≥10 years and applied structural equation models adjusted for sociodemographic confounders. Results: Data from over 22 million participants revealed that 54% were female, 53% self-identified as Pardo/Brown, and 38% were enrolled in BFP. Individuals exposed to violence, impulsivity or mental disorders showed higher risk of suicide (β=0.418, 0.246 and 0.395, respectively; all p<0.001). We confirmed that BFP mediated the association for violence and mental disorders (indirect effects: β=-0.005 and β =-0.006; p<0.001), but not for impulsivity. All models showed good fit. Conclusions: BFP was directly and indirectly associated with a reduction in suicide mortality among individuals exposed to violence and mental disorders. These analyses underscore the complex relationship between poverty alleviation and decreased suicide risk within a large administrative data set, highlighting the need to elucidate pathways to enhance suicide prevention strategies.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".