TechCare: Mobile-assessment and therapy for psychosis: An intervention for clients within the early intervention service
Bibliographic record
Abstract
Introduction In the UK, mental illness is a major source of disease burden costing in the region of £105 billion pounds. mHealth is a novel and emerging field in psychiatric and psychological care for the treatment of mental health difficulties such as psychosis. Objective To develop an intelligent real-time therapy (iRTT) mobile intervention (TechCare) which assesses participant's symptoms in real-time and responds with a personalised self-help based psychological intervention, with the aim of reducing participant's symptoms. The system will utilise intelligence at two levels: – intelligently increasing the frequency of assessment notifications if low mood/paranoia is detected; – an intelligent machine learning algorithm which provides interventions in real-time and also provides recommendations on the most popular selected interventions. Aim The aim of the current project is to develop a mobile phone intervention for people with psychosis, and to conduct a feasibility study of the TechCare App. Methods The study consists of both qualitative and quantitative components. The study will be run across three strands: – qualitative work; – test run and intervention refinement; – feasibility trial. Results Preliminary analysis of qualitative data from Strand 2 (test run and intervention refinement) in-depth interviews with service users (n = 2) and focus group with health professionals (n = 1), highlighted main themes around security of the device, multimedia and the acceptability of psychological interventions being delivered via the TechCare App. Conclusions Research in this area can be potentially helpful in addressing the demand on mental health services globally, particularly improving access to psychological interventions. Disclosure of interest The authors have not supplied their declaration of competing interest.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".