ELDERLY SUICIDE PREVENTION POLICY IN SOUTH KOREA: EVIDENCE FROM NEWS BIG DATA ANALYSIS
Bibliographic record
Abstract
Suicidal behavior is associated with a range of individual, social, and environmental factors. To establish a suicide prevention policy, identifying and understanding suicidal behaviors should be the first step. Recently, content analyses of media reports revealed substantial opportunities to identify and understand social phenomena. Therefore, this study aimed to analyze one of the prevalent social phenomena, elderly suicide, presented in Korean media reports, and examine how well the government suicide prevention policies reflected these. To achieve this aim, a news big data analysis was conducted using Korean news sources which included elderly suicide as a keyword. Since 2000, over 100,000 news articles related to elderly suicide were collected. Using “Big Kinds”, a big data analytics tool, news articles and quotations were analyzed. After analyzing the data, many of the social causes of suicidal behaviors could be determined. However, it was found that the current government suicide prevention policies were targeted mostly on reducing pathological causes such as depression and failed to reflect social causes prevalent. Therefore, this study suggests that the government should reflect various social causes affecting suicidal behaviors when developing suicide prevention policy in the future
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".