Promoting and Protecting Mental Health: A Delphi Consensus Study for Actionable Public Mental Health Messages
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.021 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it