How Can We Actually Change Help-Seeking Behaviour for Mental Health Problems among the General Public? Development of the ‘PLACES’ Model
Bibliographic record
Abstract
Good treatment uptake is essential for clinically effective interventions to be fully utilised. Numerous studies have examined barriers to help-seeking for mental health treatment and to a lesser extent, facilitators. However, much of the current research focuses on changing help-seeking attitudes, which often do not lead to changes in behaviour. There is a clear gap in the literature for interventions that successfully change help-seeking behaviour among the general public. This gap is particularly relevant for early intervention. Here we describe the development of a new model which combines facilitators to treatment and an engaging, acceptable intervention for the general public. It is called the ‘PLACES’ (Publicity, Lay, Acceptable, Convenient, Effective, Self-referral) model of treatment engagement. It is based on theoretical work, as well as empirical research on a low intensity psychoeducational cognitive behavioural therapy (CBT) intervention: one-day workshops for stress and depression. In this paper, we describe the development of the model and the results of its use among four different clinical groups (adults experiencing stress, adults experiencing depression, adolescents (age 16–18) experiencing stress, and mothers with postnatal depression). We recorded high rates of uptake by people who have previously not sought help and by racial and ethnic minority groups across all four of these clinical groups. The clinical and research implications and applications of this model are discussed.
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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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".