Exploring contextual factors impacting the implementation of and engagement with a digital platform supporting psychosis recovery: A brief report
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it