Community-based 4-level approach: Background, implementation and evidence for efficacy
Bibliographic record
Abstract
The community-based 4-level-intervention concept developed within the “European Alliance against Depression” ( http://www.eaad.net/ ) combines two important aims: to improve the care and treatment of patients with depression and to prevent suicidal behavior. It has been shown to be effective concerning the prevention of suicidal behavior [1–4] and is worldwide the most broadly implemented community-based intervention targeting depression and suicidal behavior. The 4-level intervention concept comprises training and support of primary care providers (level 1), a professional public relation campaign (level 2), training of community facilitators (teacher, priests, geriatric caregivers, pharmacists, journalists) (level 3), and support for self-help of patients with depression and for their relatives (level 4). In order to deepen the understanding of factors influencing the effectiveness of the intervention, a systematic implementation research and process analysis was performed within the EU-funded study “Optimizing Suicide Prevention Programs and Their Implementation in Europe” ( http://www.ospi-europe.com/ ; 7th Framework Programme) [5]. These analyses were based on data from four intervention and four control regions from four European countries. In addition to intervention effects on suicidal behaviour, a variety of intermediate outcomes (e.g. changes in attitude or knowledge in different populations) were considered. Strong synergistic as well as catalytic effects were identified as a result of being active simultaneously at four different levels. Predictable and unpredictable obstacles to a successful implementation of such community-based programs will be discussed. Via the EAAD, the intervention concept and materials (available in eight different languages) are offered to interested region in and outside of Europe. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.036 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.002 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".