Peer support for discharge from inpatient to community mental health services
Bibliographic record
Abstract
INTRODUCTION: In the period shortly after discharge from inpatient to community mental health care, people are at increased risk of self-harm, suicide, and readmission to hospital. Discharge interventions including peer support have shown potential, and there is some evidence that community-based peer support reduces readmissions. However, systematic reviews of peer support in mental health services indicate poor trial quality and a lack of reporting of how peer support is distinctive from other mental health support. This study is designed to establish the clinical and cost effectiveness of a peer worker intervention to support discharge from inpatient to community mental health care, and to address issues of trial quality and clarity of reporting of peer support interventions. METHODS: This protocol describes an individually randomized controlled superiority trial, hypothesizing that people offered a peer worker discharge intervention in addition to usual follow-up care in the community are less likely to be readmitted in the 12 months post discharge than people receiving usual care alone. A total of 590 people will be recruited shortly before discharge from hospital and randomly allocated to care as usual plus the peer worker intervention or care as usual alone. Manualized peer support provided by trained peer workers begins in hospital and continues for 4 months in the community post discharge. Secondary psychosocial outcomes are assessed at 4 months post discharge, and service use and cost outcomes at 12 months post discharge, alongside a mixed methods process evaluation. DISCUSSION: Clearly specified procedures for sequencing participant allocation and for blinding assessors to allocation, plus full reporting of outcomes, should reduce risk of bias in trial findings and contribute to improved quality in the peer support evidence base. The involvement of members of the study team with direct experience of peer support, mental distress, and using mental health services, in coproducing the intervention and designing the trial, ensures that we theorize and clearly describe the peer worker intervention, and evaluate how peer support is related to any change in outcome. This is an important methodological contribution to the evidence base. TRIAL REGISTRATION: This study was prospectively registered as ISRCTN 10043328 on November 28, 2016.
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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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| 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 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".