Suicide-related behaviours in schizophrenia in China: a comprehensive meta-analysis
Bibliographic record
Abstract
AIMS: Suicide-related behaviours are common in schizophrenia and are significantly associated with premature death. The objective of this meta-analysis study was to estimate the pooled prevalence of suicide-related behaviours in schizophrenia patients in China. METHODS: The relevant literature was searched systematically via the relevant electronic databases (PubMed, Embase, PsycINFO, Chinese National Knowledge Infrastructure, Wanfang Databases and Chinese Biological Medical Literature Database) from their inception until 14 September 2016. Only original studies that reported the prevalence of suicide-related behaviours including suicidal ideation (SI), suicide plan, suicide attempt (SA) and completed suicide were selected. RESULTS: Nineteen articles met the inclusion criteria and were analysed. The pooled lifetime prevalence of SI and SA were 25.8% (95% CI 14.7-41.1%) and 14.6% (95% CI 9.1-22.8%), respectively. The 1-month prevalence of SI was 22.0% (95% CI 18.2-26.4%). Subgroup analyses of lifetime SI and SA showed that gender, sample size, survey year, study location and source of patients have no significant mediating effects on the results. CONCLUSIONS: Suicide-related behaviours are common in Chinese schizophrenia patients. Due to the high mortality risk, regular screening and effective suicide prevention programmes are warranted.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.024 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 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".