Shifting the Focus: A Case for a Positive Mental Health Framework in Europe
Bibliographic record
Abstract
This open letter advocates for a paradigm shift in European mental health policy by proposing the adoption of a European Positive Mental Health Framework. It argues that current approaches remain overly focused on severe mental health problems and institutional care, while overlooking the broader continuum of mental health. This includes mental wellbeing aspects such as emotional resilience, life satisfaction, and social connectedness. The authors highlight the need for robust, strength-based surveillance systems that track the core components and the enabling conditions of mental wellbeing. Drawing from international models such as Canada's Positive Mental Health Surveillance Indicator Framework and the OECD’s Well-being Framework, the letter underscores the feasibility and importance of coordinated, cross-country monitoring. Despite challenges in standardizing definitions and metrics, the letter emphasizes that harmonized data collection is essential for designing responsive and preventative mental health systems. It calls on the EU to build on existing scientific and policy initiatives, including the European Public Health Alliance’s indicator development, to create a framework that enables both comprehensive measurement and actionable change. By shifting focus toward positive mental health, the EU can not only better prevent illness but also enhance societal wellbeing resilience in the face of future crises.
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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 | 0.052 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.029 |
| Scholarly communication | 0.018 | 0.017 |
| Open science | 0.003 | 0.026 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.003 | 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".