Early Psychosis Service User Views on Digital Technology: Qualitative Analysis
Bibliographic record
Abstract
BACKGROUND: Digital technology has the potential to improve outcomes for people with psychosis. However, to date, research has largely ignored service user views on digital health interventions (DHIs). OBJECTIVE: The objective of our study was to explore early psychosis service users' subjective views on DHIs. METHODS: Framework analysis was undertaken with data obtained from 21 semistructured interviews with people registered with early intervention for psychosis services. Robust measures were used to develop a stable framework, including member checking, triangulation, independent verification of themes, and consensus meetings. RESULTS: The following 4 themes were established a priori: acceptability of technology in psychosis and mental health; technology increasing access to and augmenting mental health support; barriers to adopting DHIs; and concerns about management of data protection, privacy, risk, and security of information. The following 2 themes were generated a posteriori: blending DHIs with face-to-face treatment and empowerment, control, and choice. DHIs were also viewed as potentially destigmatizing, overcoming barriers faced in traditional service settings, facilitating communication, and empowering service users to take active control of their health care. CONCLUSIONS: In the first study of its kind, early psychosis service users' were largely positive about the potential use of DHIs supporting and managing mental health. Overall, service users felt that DHIs were a progressive, modern, and relevant platform for health care delivery. Concerns were expressed around privacy and data security and practical barriers inherent within DHIs, all of which require further attention. Future research should explore whether findings transfer to other service user groups, other technology delivery formats, and across a range of treatment modalities.
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.
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 | 0.018 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".