Developing an accessible and effective public mental health programme for members of the general public
Bibliographic record
Abstract
Promoting mental health and meeting the needs of the large numbers of the general public with problems of anxiety and depression is a big challenge. Particular difficulties are the low capacity of the therapy services and the reluctance of the general public to seek help. The aim of this study was to compare the attendance, effectiveness and characteristics of participants self‐referring to six different psycho‐educational workshops, each using non‐diagnostic titles: self‐confidence; stress; sleep; relationships; happiness; and anger. The series of day‐long workshops ran for one year and were offered to members of the general public in south east London. Over a quarter had not previously sought help from their GP. The take‐up rates for the self‐confidence, sleep and anger workshops were highest and one month after attending these workshops, participants reported significantly lower depression and distress. It was concluded that a self‐referral route to some day‐long workshops can attract quite large numbers of the general public and provide access to effective psychological treatment. These workshops can be used as an effective way of promoting mental health and improving the provision of evidence‐based mental health treatment in the community, possibly within the Improving Access to Psychological Treatments (IAPT) programme in the UK.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".