How to Decrease Suicide Rates in Both Genders? An Effectiveness Study of a Community-Based Intervention (EAAD)
Bibliographic record
Abstract
BACKGROUND: The suicide rate in Hungary is high in international comparison. The two-year community-based four-level intervention programme of the European Alliance Against Depression (EAAD) is designed to improve the care of depression and to prevent suicidal behaviour. Our aim was to evaluate the effectiveness of a regional community-based four-level suicide prevention programme on suicide rates. METHOD: The EAAD programme was implemented in Szolnok (population 76,311), a town in a region of Hungary with an exceptionally high suicide rate. Effectiveness was assessed by comparing changes in suicide rates in the intervention region after the intervention started with changes in national suicide rates and those in a control region (Szeged) in the corresponding period. RESULTS: For the duration of the programme and the follow-up year, suicide rates in Szolnok were significantly lower than the average of the previous three years (p = .0076). The suicide rate thus went down from 30.1 per 100,000 in 2004 to 13.2 in 2005 (-56.1 %), 14.6 in 2006 (-51.4 %) and 12.0 in 2007 (-60.1 %). This decrease of annual suicide rates in Szolnok after the onset of the intervention was significantly stronger than that observed in the whole country (p = .017) and in the control region (p = .0015). Men had the same decrease in suicide rates as women. As secondary outcome, an increase of emergency calls to the hotline service (200%) and outpatient visits at the local psychiatry clinic (76%) was found. CONCLUSIONS: These results seem to provide further support for the effectiveness of the EAAD concept. Whilst the majority of suicide prevention programs mainly affect female suicidal behaviour, this programme seems to be beneficial for both sexes. The sustainability and the role of the mediating factors (social service and health care utilization, community attitudes about suicide) should be key points in future research.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".