Promoting and Protecting Mental Health: A Delphi Consensus Study for Actionable Public Mental Health Messages
Bibliographic record
Abstract
PURPOSE: Public health campaigns are still relatively rare in mental health. This paper aims to find consensus on the preventive self-management actions (i.e. "healthy behaviors") for common mental health problems (e.g. depression and anxiety) that should be recommended in mental health campaigns directed at the general public. APPROACH: A 3-round Delphi study. PARTICIPANTS: 23 international experts in mental health and 1447 members of the public, most of whom had lived experience of mental health problems. METHOD: The modified Delphi study combined quantitative and qualitative data collection: 1) online qualitative survey data collection thematically analyzed, 2) recommendations rated for consensus, 3) consensus items rated by public panel on a Likert scale. RESULTS: Expert consensus was reached on 15 behaviors that individuals can engage in to sustain mental health. Eight were rated as appropriate by more than half (50%) of the public panel, including: avoiding illicit drugs (80%, n = 1154), reducing debt (72%, n = 1043), improving sleep (69%, n = 1000), regulating mood (65%, n = 941), having things to look forward to (60%, n = 869). CONCLUSIONS: A series of healthy behaviors for the promotion and protection of mental health received expert and public consensus. To our knowledge, this is the first study to offer a set of actions for public health messaging for the prevention of poor mental health. Future research should focus on evaluating effectiveness of these actions in a universal primary prevention context.
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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 | 0.191 | 0.171 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".