Feasibility and acceptability of web-based enhanced relapse prevention for bipolar disorder (ERPonline): Trial protocol
Bibliographic record
Abstract
BACKGROUND: Relapse prevention interventions for Bipolar Disorder are effective but implementation in routine clinical services is poor. Web-based approaches offer a way to offer easily accessible access to evidence based interventions at low cost, and have been shown to be effective for other mood disorders. METHODS/DESIGN: This protocol describes the development and feasibility testing of the ERPonline web-based intervention using a single blind randomised controlled trial. Data will include the extent to which the site was used, detailed feedback from users about their experiences of the site, reported benefits and costs to mental health and wellbeing of users, and costs and savings to health services. We will gain an estimate of the likely effect size of ERPonline on a range of important outcomes including mood, functioning, quality of life and recovery. We will explore potential mechanisms of change, giving us a greater understanding of the underlying processes of change, and consequently how the site could be made more effective. We will be able to determine rates of recruitment and retention, and identify what factors could improve these rates. DISCUSSION: The findings will be used to improve the site in accordance with user needs, and inform the design of a large scale evaluation of the clinical and cost effectiveness of ERPonline. They will further contribute to the growing evidence base for web-based interventions designed to support people with mental health problems.
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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 | 0.050 | 0.051 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.074 | 0.014 |
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".