Exploring contextual factors impacting the implementation of and engagement with a digital platform supporting psychosis recovery: A brief report
Bibliographic record
Abstract
Individuals with schizophrenia often demonstrate poor engagement in treatment and challenges with illness self-management. App4independence (A4i) is a digital health platform that was developed with the purpose of addressing the aforementioned challenges. While digital interventions can support patient care, there is a paucity of research on implementing such interventions in clinical settings. To describe the contextual factors that impacted the implementation of and engagement with A4i across three different clinical implementation sites, a descriptive approach, guided by implementation science frameworks, was employed to understand how people, culture, process, and technology impacted the implementation of A4i. Descriptive statistics were used to present user engagement data across each site implementation. Additionally, the lessons learned from each implementation were described narratively. Overall, 53 patients were onboarded to A4i in Context 1, 8 in Context 2, and 65 within Context 3, with retention rates over 90 days of 100%, 100%, and 96%, respectively. The adoption, engagement, and sustained use of the A4i platform varied across each implementation site and were affected by implementation strategies within the sociotechnical domains of people, culture, process, and technology. Despite differences in implementation processes, engagement with A4i remained consistently high. Customized educational materials, digital navigators, and technical support served as facilitators in the adoption of A4i.
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".