A Peer-Led Electronic Mental Health Recovery App in a Community-Based Public Mental Health Service: Pilot Trial
Bibliographic record
Abstract
BACKGROUND: There is an increasing need for peer workers (people with lived experience of mental health problems who support others) to work alongside consumers to improve recovery and outcomes. In addition, new forms of technology (tablet or mobile apps) can deliver services in an engaging and innovative way. However, there is a need to evaluate interventions in real-world settings. OBJECTIVE: This exploratory proof-of-concept study aimed to determine if a peer worker-led electronic mental health (e-mental health) recovery program is a feasible, acceptable, and effective adjunct to usual care for people with moderate-to-severe mental illness. METHODS: Overall, 6 consumers and 5 health service staff participated in the evaluation of a peer-led recovery app delivered at a community-based public mental health service. The peer worker and other health professional staff invited attendees at the drop-in medication clinics to participate in the trial during June to August 2017. Following the intervention period, participants were also invited by the peer worker to complete the evaluation in a separate room with the researcher. Consumers were explicitly informed that participation in the research evaluation was entirely voluntary. Consumer evaluation measures at postintervention included recovery and views on the acceptability of the program and its delivery. Interviews with staff focused on the acceptability and feasibility of the app itself and integrating a peer worker into the health care service. RESULTS: Consumer recruitment in the research component of the study (n=6) fell substantially short of the target number of participants (n=30). However, from those who participated, both staff and consumers were highly satisfied with the peer worker and somewhat satisfied with the app. Health care staff overall believed that the addition of the peer worker was highly beneficial to both the consumers and staff. CONCLUSIONS: The preliminary findings from this proof-of-concept pilot study suggest that a peer-led program may be a feasible and acceptable method of working on recovery in this population. However, the e-mental health program did not appear feasible in this setting. In addition, recruitment was challenging in this particular group, and it is important to note that these study findings may not be generalizable. Despite this, ensuring familiarity of technology in the target population before implementing e-mental health interventions is likely to be of benefit.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".