EMPOWER in Daily Life: A Qualitative Investigation of End User Experience of a blended digital intervention for relapse prevention in schizophrenia in a cluster randomised controlled feasibility trial.
Bibliographic record
Abstract
Objectives: To study the end-user experiences of participants randomised to receive a blended digital intervention for relapse prevention in schizophrenia in the context of a cluster randomised controlled feasibility trial.Design: A qualitative interview design with thematic analysis was used.Method: A subsample of EMPOWER participants comprising 16 patients, 5 mental health staff and one carer were interviewed one-on-one.Results: Two overarching themes were constructed that were relevant for understanding end-user experiences within the EMPOWER trial: Affordances and Change Processes. Affordances described the processes underpinning how and why participants interacted with or avoided the various components of the intervention. Affordances spanned all EMPOWER components, including self-monitoring, peer support workers, clinical triaging, self-management messages and diary function. The affordances were Access to Social Connection, Access to Digital, Access to Mental Health Support, the Ability to Gauge Mental Health and Access to Mental Health Information. The affordances framework helped explain the multitude of engagement trajectories featured within the qualitative interviews. If participants sustained usage, affordances acted as a springboard for change processes, including increased self-confidence that patients could self-manage, noticing patterns and changes, and using EMPOWER as a conversation starter.Conclusions: The implementation process of EMPOWER was emergent and was best described by the intervention offering a range of affordances which could act as implementation barriers or facilitators depending on individual needs and wants. Affordances may present a sound theoretical framework for explaining end-user experiences.
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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 | 0.043 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".