The implementation and first insights of the French-speaking Swiss programme for monitoring self-harm
Bibliographic record
Abstract
AIMS OF THE STUDY: Self-harm is a major risk factor for suicide but remains poorly documented. No data on self-harm in French-speaking Switzerland exist. To address this deficiency, the Swiss Federal Office of Public Health commissioned a specific self-harm monitoring programme. We present and discuss its implementation and first findings. METHODS: Every patient aged 18–65 years presenting for self-harm to the emergency departments of the Lausanne and Neuchâtel general hospitals were included in the monitoring programme over a 10-month period (December 2016 to September 2017). Clinicians collected anonymous sociodemographic and clinical data. RESULTS: The sample included 490 patients (54.9% female and 45.1% male) for 554 episodes of self-harm, showing a higher proportion of patients aged 18–34 (49.2%) than older age groups (35–49, 33.7% and 50–65, 17.1%). Patients were mostly single (56.1%) and in problematic socioeconomic situations (65.7%). Self-poisoning was the most commonly used method (58.2%) and was preferred by women (71% of females and 42.5% of males, Fisher’s exact test, p <0.001) and the majority of patients (53.3%) had experienced at least one previous episode of self-harm. The self-harm rate was 220 per 100,000 inhabitants in Lausanne and 140 in Neuchâtel. Suicidal intent was clear for 50.6% of the overall sample, unclear for 25.1% and absent for 24.3%. It differed significantly between sites (χ2(2) = 9.068, p = 0.011) as Lausanne reported more incidents of unclear intent (27.7% versus 17.4% in Neuchâtel) and Neuchâtel more incidents with absence of intent (33.1% versus 21.3% in Lausanne). In Lausanne, patients more frequently resorted to methods such as jumping from a height (11.4%) and hanging (9%) than in Neuchâtel (1.6% and 4.9%, Fisher’s exact test, p = 0.006). CONCLUSIONS: Our results are globally consistent with previous research on self-harm. We found significant inter-site differences in methods, suicidal intent and self-harm rates. Our findings highlight the importance of implementing local self-harm monitoring to identify specific at-risk groups and develop targeted preventive intervention.
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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 | 0.015 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".