Why Do People Live or Die? A Retrospective Study from a Crisis Intervention Clinic in North India
Bibliographic record
Abstract
Background: Suicide results from complex interactions of various risk factors—reasons for dying (RFD)—and protective factors—reasons for living (RFL). Suicide is not necessarily a wish to die but may be an appeal for help. We analyzed RFD and RFL in persons who had attempted suicide, through their clinical records at a Crisis Intervention Clinic (CIC). Methods: We retrospectively analyzed demographic and clinical data, and classified RFD and RFL, among patients with either ideas or attempt of suicide registered at our CIC ( N = 83). Using two open-ended questions from the clinical history data, we derived their RFD or RFL; ( n = 53) completed these questions regarding RFD-RFL. Results: In the total sample, males and females were equally represented and educated, but males were significantly older. Most common diagnosis was nonpsychotic mood disorder. Commonest mode of suicide attempt was hanging. Family conflict vs. family responsibility, hope vs. hopelessness, stressful life events, and negative cognitions about the self and the world were important RFD. RFL included feeling responsible, love for family and for self, hope, career success, and religious beliefs, Conclusion: RFD and RFL could both be grouped in similar categories related to family, career, hope, etc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".