Mental Health Professionals’ Technology Usage and Attitudes Toward Digital Health for Psychosis: Comparative Cross-Sectional Survey Study
Bibliographic record
Abstract
Background: Digital health technologies (DHTs) for psychosis have been developed and tested rapidly in recent years. However, research examining mental health professionals' views on the use of DHTs for people with psychosis is limited. Given the increased accessibility and availability of DHTs for psychosis, an up-to-date understanding of staff perception of DHTs for psychosis is warranted. Objective: In this study, we aimed to investigate mental health professionals' usage of technology and their perception of service users' technology usage; their views toward the use of DHTs for psychosis, including their concerns; and barriers for implementing DHTs in a mental health setting. Methods: Two cross-sectional surveys were distributed to mental health care staff who had experience of working with individuals experiencing psychosis in the United Kingdom. Survey 1 (from April 2018 to September 2020) was distributed to 3 local UK National Health Service (NHS) trusts in Northwest England; survey 2 was administered nationally across 31 UK NHS trusts or health boards (from November 2022 to March 2024). The COVID-19 pandemic occurred between the 2 survey periods. Data were analyzed descriptively. Results: A total of 155 and 352 participants completed surveys 1 and 2, respectively. Staff reported high levels of technology ownership and usage in both surveys. In general, staff expressed positive views regarding the use of DHTs for psychosis; however, barriers and concerns, including affordability, digital literacy, and potential negative effects on service users' mental health, were also reported. We did not find notable changes in terms of staff use of digital technology in clinical practice over time. Conclusions: Staff sampled from a broad and diverse range expressed consistent optimism about the potential implementation of DHTs in practice, though they also noted some concerns regarding safety and access. While the COVID-19 pandemic is frequently regarded as a catalyst for the adoption of digital health care tools, the sustainability of this transition from traditional to digital health care appeared to be suboptimal. To address staff concerns regarding safety and potentially facilitate the implementation of DHTs, systematic evaluation of adverse effects of using DHTs and dissemination of evidence are needed. Organizational support and training should be offered to staff to help address barriers and increase confidence in recommending and using DHTs with service users.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".