Bibliographic record
Abstract
The present study aims to examine the effective causes of suicide in Zahedan city, Iran 2014-2015. In fact, suicide is a dangerous behavior to end the life, which would impose a social problem and cost a lot of harm to individuals, families and society. The main question is that what are the factors contributing to the suicide? Important assumptions include: marriage and having children reduces the suicide attempts. Education and employment could reduce the incidence of suicide attempts and suicide attempts are higher at a young age. This study makes use of fieldwork and analytical methods. The population of this research included people who committed suicide during nine months, from Azar 2014 to September 2015 and have been referred to Khatamolanbia (PBUH) hospital, Imam Ali (AS) hospital and Zahedan's forensic medicine. The findings suggest that all of 71 samples were Muslims; 36 persons were women (50.7%) and 35 persons were men (49.3%).There isn't any relationship between mental disorders and suicide attempts. The incidence of suicide among people 16 to 30 years old with 83.2% is much more than other age groups. Suicide attempts rates among educated people are less than under diploma with 66.2%. There is no significant relationship between marital status and suicide, but suicide (67.6%) among those without children is more than people with a family. Suicide attempts among people with higher-income levels and favorable socio-economic conditions and poor people with 15.5%, is much less than middle-income and good-income with 84.5%. 29.6% of those surveyed, that is 21 people, who attempted suicide had died.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".