Telehealth interventions for schizophrenia-spectrum disorders and clinical high-risk for psychosis individuals: A scoping review
Bibliographic record
Abstract
BACKGROUND: Despite its increased use in mental health, both health care provision by telehealth and research are in the early stages. Videoconferencing, a telehealth subfield, has been mainly used for the medication management and delivery of psychological treatments for mood, adjustment and anxiety disorders, and to a lesser extent for psychotic disorders. OBJECTIVES: The focus of this scoping review is on studies using videoconferencing for intervention for individuals with a diagnosis of schizophrenia-spectrum disorder and those who may be considered to be in the very early stages of psychosis (clinical high risk). The aim of this review is to assess the feasibility, acceptability and clinical benefits of videoconferencing interventions and compare them with face-to-face interventions for this population. METHODS: A scoping review of peer-reviewed original research on the use of videoconferencing for intervention purposes in individuals with a schizophrenia-spectrum disorder or at clinical high risk. RESULTS: = 439 individuals). There was no study reporting on videoconferencing interventions for individuals at clinical high risk. All the studies reported that videoconferencing implementation was feasible, and most of them described high acceptance by individuals with a schizophrenia-spectrum disorder. However, selection bias of studies was high, and overall methodological quality was poor. CONCLUSION: Videoconferencing interventions seem feasible for participants with schizophrenia-spectrum disorder who showed high acceptance of this intervention modality.
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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 | 0.006 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".