mHealth based interventions for the assessment and treatment of psychotic disorders: a systematic review
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
The relative burden of mental health disorders is increasing globally, in terms of prevalence and disability. There is limited data available to guide treatment choices for clinicians in low resourced settings, with mHealth technologies being a potentially beneficial avenue to bridging the large mental health treatment gap globally. The aim of the review was to search the literature systematically for studies of mHealth interventions for psychosis globally, and to examine whether mHealth for psychosis has been investigated. A systematic literature search was completed in Embase, Medline, PsychINFO and Evidence Based Medicine Reviews databases from inception to May 2016. Only studies with a randomised controlled trial design that investigated an mHealth intervention for psychosis were included. A total of 5690 records were identified with 7 studies meeting the inclusion criteria. The majority of included studies, were conducted across Europe and the United Sates with one being conducted in China. The 7 included studies examined different parameters, such as Experiential Sampling Methodology (ESM), medication adherence, cognitive impairment, social functioning and suicidal ideation in veterans with schizophrenia. Considering the increasing access to mobile devices globally, mHealth may potentially increase access to appropriate mental health care. The results of this review show promise in bridging the global mental health treatment gap, by enabling individuals to receive treatment via their mobile phones, particularly for those individuals who live in remote or rural areas, areas of high deprivation and for those from low resourced settings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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