Contact of Mental and Nonmental Health Care Providers Prior to Suicide in Taiwan: A Population-Based Study
Bibliographic record
Abstract
OBJECTIVE: Higher rates of health care service use prior to suicide were previously reported in Western countries; however, these studies have tended to suffer from small sample sizes. This nationwide, population-based study examines the distribution and patterns of health care service use among suicide victims in Taiwan. METHOD: A retrospective cohort study was conducted using linked population-based data to determine the proportion of health care service use among suicide victims aged 15 years and older within the 1-year and 1-month period prior to their deaths. After adjusting for demographic, socioeconomic and health care indices, the differences in health care service use patterns were assessed for age and sex. RESULTS: Among the 19 426 suicide victims in the sample, 83.1% had used nonmental health care services within the 1-year period prior to their death, while only 22.2% had used mental health care services. Men, and suicide victims aged 55 years and older, were less likely to have had any contact with mental health care professionals prior to their deaths (P < 0.001). CONCLUSIONS: In line with prior studies, similarly high rates and distinct patterns of health care service use were found in Taiwan prior to suicide. These findings will be of practical interest and should support designing appropriate methods of suicide intervention and effective preventive strategies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".