Analyzing 14-years of suicide rates in Chile: Impact of alcohol policy, domestic violence, and a suicide prevention program
Bibliographic record
Abstract
Suicide is a major public health problem worldwide with far-reaching effects on families, communities, and societies. Influencing factors range from macro-level interventions like alcohol control policies and suicide prevention programs to individual contributors such as alcohol abuse and domestic violence. This study aimed to examine the relationship between Chile's suicide rate changes from 2002 to 2015 and the Alcohol Act of 2004, a national suicide prevention program implemented in 2007, alcohol abuse, and domestic violence. Assembling a unique longitudinal dataset from Chilean public institutions, the study employed an instrumental variable time-series cross-regional design. Results indicated that the Alcohol Act was not associated with suicide rates, domestic violence exhibited a significant association with increased suicide rates, and the national suicide prevention program was linked to reductions in suicide rates, especially among males. These findings align with research from neighbouring countries, showcasing the efficacy of suicide prevention programs in decreasing suicide rates in Chile. Results highlight the importance of integrating protocols to early-detect domestic violence in suicide prevention programs, as well as the need to further improving alcohol control policies to complement suicide prevention programs.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".