A Peer-Led Electronic Mental Health Recovery App in an Adult Mental Health Service: Study Protocol for a Pilot Trial
Bibliographic record
Abstract
BACKGROUND: There is growing demand for peer workers (people who use their own lived experience to support others in their recovery) to work alongside consumers to improve outcomes and recovery. Augmenting the workforce with peer workers has strong capacity to enhance mental health and recovery outcomes and make a positive contribution to the workforce within mental health systems and to the peer workers themselves. Technology-based applications are highly engaging and desirable methods of service delivery. OBJECTIVE: This project is an exploratory proof-of-concept study, which aims to determine if a peer worker-led electronic mental (e-mental) health recovery program is a feasible, acceptable, and effective adjunct to usual treatment for people with moderate to severe mental illness. METHODS: The study design comprises a recovery app intervention delivered by a peer worker to individual consumers at an adult mental health service. Evaluation measures will be conducted at post-intervention. To further inform the acceptability and feasibility of the model, consumers will be invited to participate in a focus group to discuss the program. The peer worker, peer supervisor, and key staff at the mental health service will also be individually interviewed to further evaluate the feasibility of the program within the health service and further inform its future development. RESULTS: The program will be delivered over a period of approximately 4 months, commencing June 2017. CONCLUSIONS: If the peer worker-led recovery app is found to be feasible, acceptable, and effective, it could be used to improve recovery in mental health service consumers.
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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 | 0.042 | 0.035 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.008 | 0.005 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.081 | 0.018 |
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".