Bibliographic record
Abstract
PURPOSE OF REVIEW: Mental disorders take a major toll, economically, socially, and psychologically, on individuals, families, and societies. Prevention provides an important and realistic opportunity to overcome this major health problem. This review outlines a conceptual framework for mental health prevention and effective strategies and programs for the prevention of mental disorders. RECENT FINDINGS: Risk and protective factors for mental illness provide leverage points for prevention interventions. A life course perspective, looking at disease from conception, pregnancy, parenting, infancy, childhood, adolescence, adulthood to aging, emphasizes the importance of targeting prevention efforts as early as possible in life. Currently available effective and realistic preventions targeting major phases of life including both universal (community) and selective high-risk approaches are noted. The Internet and its associated technologies are seen to have great potential for prevention. SUMMARY: Common mental disorders are preventable, and prevention is cost-effective. Although the evidence base for the prevention of mental disorders needs to be expanded with rigorous large-scale pragmatic trials of promising effective programs, we have at our disposal strong evidence and effective tools on which to base prevention efforts. These facts need to be fully communicated to providers, policy makers, and the population at large, and acted upon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".