Non-presentation at hospital following a suicide attempt: a national survey
Bibliographic record
Abstract
BACKGROUND: A few previous studies suggest that a large number of individuals do not present at hospital following a suicide attempt, complicating recurrence prevention and prevalence estimation. METHODS: Data were extracted from a regular phone survey in representative samples of the French population aged 18-75 years old. Five surveys between 2000 and 2017 collected data about the occurrence of a previous suicide attempt and subsequent care contacts. A total of 102,729 individuals were surveyed. Among them, 6,500 (6.4%) reported a lifetime history of suicide attempt. RESULTS: Following their last suicide attempt, 39.3% reported they did not present to hospital (53.4% in 18-24 year-olds), with limited changes in rates with time. Risk factors for non-presentation were being male [adjusted odds ratio = 1.3, 95% confidence interval (1.1-1.5)], living with someone [1.2 (1.0-1.4)], being a non-smoker [1.4 (1.2-1.6)], and being younger at time of attempt [0.97 (0.96-0.98) per year]. Of those who did not present to hospital, only 37.7% reported visiting a doctor or a psychiatrist/psychologist after their act v. 67.1% in those who presented to hospital (as a second health contact). In both cases, half disclosed their act to someone else. Prevalence rates of suicide attempts reported in community were 4.6 times higher than those in hospital administrative databases. CONCLUSIONS: This survey at a national level confirmed that a large proportion of individuals does not go to the hospital and does not meet any health care professionals following a suicidal act. Assessment of unmet needs is necessary.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".