Process evaluation of digital mental health interventions for psychosis: A scoping review with framework synthesis. (Preprint)
Bibliographic record
Abstract
BACKGROUND: Implementing digital mental health interventions (DMHI) for those with psychosis is a persistent challenge. A process evaluation, or studies conducted alongside trials, is one research method that may address this issue. However, a synthesis of process evaluation data in this area is missing. OBJECTIVE: This study aimed to understand what is known about context, implementation, and mechanisms of impact by synthesizing process evaluation data from trials evaluating DMHIs used by people with psychosis. METHODS: A scoping review using a 2-phase search strategy underpinned by the Medical Research Council (MRC) process evaluation framework was conducted. Database searches of Cochrane Central Register of Controlled Trials and PsycInfo in 2024 and 2025 first identified an index sample of peer-reviewed trials predominantly conducted in the United Kingdom (≥50% of samples from the United Kingdom in multicountry studies). Next, papers linked to the index sample were retrieved and included if they reported process evaluation data as operationalized in the MRC framework. Two authors independently screened references, extracted summary data, and assessed the quality of index trials. One author qualitatively synthesized process evaluation data using a deductive framework synthesis approach using the MRC framework. Findings were triangulated with senior authors and presented as a narrative synthesis. RESULTS: Searches identified 14 DMHIs and 45 papers reporting process evaluation data, though only 2 were labeled as such. Qualitative syntheses of process evaluation data generated five themes aligned with the MRC framework: (1) enhancing fit and supporting delivery (implementation strategies); (2) DMHI implementation varied across users, staff, and delivery settings (implementation outcomes); (3) helping users to respond in more helpful ways (mechanisms); (4) addressing perceived and actual implementation factors (context); and (5) limited impact of user characteristics on DMHI outcomes (context). CONCLUSIONS: There is preliminary evidence that DMHIs can be delivered to people experiencing psychosis within trial settings, although use varied between individuals. Future implementation efforts may benefit from addressing contextual factors influencing DMHI use, including users' treatment needs and preferences, everyday demands, and staff availability for blended interventions. Future research could evaluate implementation strategies, validate how and for whom DMHIs work, and embed process evaluation in trials. TRIAL REGISTRATION: PROSPERO CRD42024439117; https://www.crd.york.ac.uk/PROSPERO/view/CRD42024439117.
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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 | 0.129 | 0.278 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".