Determinants of Suicidal Ideation and Behavior, Economic Theories of Suicidal Behavior and the Economics of Prevention
Bibliographic record
Abstract
Suicide has long history dating back to the earliest historical records of humankind. Currently, the number of people committing suicide around the world is not negligible. In 2010, almost one million people committed suicide, which corresponds to a mortality rate of 16 per 100,000 people. There are also significant numbers of suicide attempts for every completed suicide especially for young people. The economics literature documents a substantial cost of suicide. These cost includes ambulance services, hospitalization, autopsy services and other healthcare and mental health services for the individual who dies as well as his family members, friends and significant others. Additional economic costs include the value of life lost, and productivity loss. Economists have started to study suicide, including its economic determinants and the economic evaluation of suicide prevention programs. In this chapter, we provide a brief review of these two areas. The lessons learned directions for further research are highlighted in the chapter.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".