Using EMPOWER in daily life: a qualitative investigation of implementation experiences
Bibliographic record
Abstract
BACKGROUND: Digital self-management tools blended with clinical triage and peer support have the potential to improve access to early warning signs (EWS) based relapse prevention in schizophrenia care. However, the implementation of digital interventions in psychosis can be poor. Traditionally, research focused on understanding how people implement interventions has focused on the perspectives of mental health staff. Digital interventions are becoming more commonly used by patients within the context of daily life, which means there is a need to understand implementation from the perspectives of patients and carers. METHODS: Semi-structured one-on-one interviews with 16 patients who had access to the EMPOWER digital self-management intervention during their participation in a feasibility trial, six mental health staff members who supported the patients and were enrolled in the trial, and one carer participant. Interviews focused on understanding implementation, including barriers and facilitators. Data were coded using thematic analysis. RESULTS: The intervention was well implemented, and EMPOWER was typically perceived positively by patients, mental health staff and the carer we spoke to. However, some patients reported negative views and reported ideas for intervention improvement. Patients reported valuing that the app afforded them access to things like information or increased social contact from peer support workers that went above and beyond that offered in routine care. Patients seemed motivated to continue implementing EMPOWER in daily life when they perceived it was creating positive change to their wellbeing, but seemed less motivated if this did not occur. Mental health staff and carer views suggest they developed increased confidence patients could self-manage and valued using the fact that people they support were using the EMPOWER intervention to open up conversations about self-management and wellbeing. CONCLUSIONS: The findings from this study suggest peer worker supported digital self-management like EMPOWER has the potential to be implemented. Further evaluations of these interventions are warranted, and conducting qualitative research on the feasibility gives insight into implementation barriers and facilitators, improving the likelihood of interventions being usable. In particular, the views of patients who demonstrated low usage levels would be valuable.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".