Partnerships for better mental health worldwide: WPA recommendations on best practices in working with service users and family carers
Bibliographic record
Abstract
WPA President M. Maj established the Task Force on Best Practice in Working with Service Users and Carers in 2008, chaired by H. Herrman. The Task Force had the remit to create recommendations for the international mental health community on how to develop successful partnership working. The work began with a review of literature on service user and carer involvement and partnership. This set out a range of considerations for good practice, including choice of appropriate terminology, clarifying the partnership process and identifying and reducing barriers to partnership working. Based on the literature review and on the shared knowledge in the Task Force, a set of ten recommendations for good practice was developed. These recommendations were the basis for a worldwide consultation of stakeholders with expertise as service users, families and carers, and the WPA Board and Council. The results showed a strong consensus across the international mental health community on the ten recommendations, with the strongest agreement coming from service users and carers. This general consensus gives a basis for Task Force plans to seek support for activities to promote shared work worldwide to identify best practice examples and create a resource to assist others to begin successful collaboration.
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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 | 0.085 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.007 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.017 | 0.025 |
| Open science | 0.013 | 0.037 |
| Research integrity | 0.047 | 0.034 |
| Insufficient payload (model declined to judge) | 0.035 | 0.017 |
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".