Depression As a Strong Prediction of Suicide Risk
Bibliographic record
Abstract
Suicide happens throughout one’s life and is a serious health problem. World Health Organization (WHO) placed suicidal problem as society health priority. Many things is related to suicide, among others mental disorders like depression, anxiety, alcohol consumption behavior, life pressure experienced, financial problem, personal relation, or so is chronic illness experienced, conflict occurred, disaster, harassment, alienation social demography characteristic. Objective research to identify the risk factor of suicidal thought in several regions in Indonesia. The study design was Cross sectional research conducted in 3 (three) districts/cities in Indonesia. Proportional illustration Sample taken conducted using stratified random sampling. Fixed variables analyzed such as the suicidal thoughts with the independent variable are gender, age, marital status, education, employment, ownership statistic, depression, anxiety. Univariate, bivariate and multivariate using the SPSS software. The result of the research showed that suicidal behavior happened more often to depressed individual, lived in the cities, anxious, productive age group, has low education level. Respondent suffered through depression 11 times more likely to have suicidal thoughts. The risk of suicidal behavior also increased 5 times to respondent in the cities. Respondent living anxiety 2 times riskier to have suicidal intent. Blue collar respondent also 2 times more risk in comparison to civil servants to have suicidal intent. Therefore it can be concluded that The influence of psychological factors is more substantial to inflict suicidal behavior, even though there is also effect of the social economy factor.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".