A survey of eMedia-delivered interventions for schizophrenia used in randomized controlled trials
Bibliographic record
Abstract
Farooq Naeem,1 Tariq Munshi,1 Shuo Xiang,1 Megan Yang,1 Farhad Shokraneh,2 Yumeen Syed,3 Muhammad Ayub,1 Clive E Adams,4 Saeed Farooq5 1Department of Psychiatry, Queens University, Kingston, ON, Canada; 2Institute of Mental Health, Nottingham, UK; 3Trenlogic, Toronto, ON, Canada; 4Nottingham University, Nottingham, 5Keele University, Stafford, UK Background: Randomized trials evaluating electronic Media (eMedia) delivery of interventions are increasingly frequent in mental health. Although a number of reviews have reported efficacy of these interventions, none has reviewed the type of eMedia interventions and quality of their description. We therefore decided to conduct a survey of eMedia-delivered interventions for schizophrenia.Methods: We surveyed all relevant trials reliably identified in the Cochrane Schizophrenia Group’s comprehensive register of trials by authors working independently. Data were extracted regarding the size of the trial, interventions, outcomes and how well the intervention was described.Results: eMedia delivery of interventions is increasingly frequent in trials relevant to the care of people with schizophrenia. The trials varied considerably in sample sizes (mean =123, median =87, range =20–507), and interventions were diverse, rarely evaluating the same approaches and were poorly reported. This makes replication impossible. Outcomes in these studies are limited, have not been noted to be chosen by end users and seem unlikely to be easy to apply in routine care. No study reported on potential adverse effects or cost, end users satisfaction or ease of use. None of the papers mentioned the use of CONSORT eHealth guidelines.Conclusion: There is a need to improve reporting and testing of psychosocial interventions delivered by eMedia. New trials should comply with CONSORT eHealth guidance on design, conduct and reporting, and existing CONSORT should be updated regularly, as the field is constantly evolving. Keywords: randomized trials, survey, eMedia, information technology, schizophrenia, methodology
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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 | 0.379 | 0.730 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.008 | 0.008 |
| Bibliometrics | 0.025 | 0.035 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".