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Record W2152126822 · doi:10.1016/j.cct.2015.01.004

Feasibility and acceptability of web-based enhanced relapse prevention for bipolar disorder (ERPonline): Trial protocol

2015· article· en· W2152126822 on OpenAlexaff
Fiona Lobban, Alyson Dodd, Dave Dagnan, Peter J. Diggle, Martin Griffiths, Bruce Hollingsworth, David A. Knowles, Rita Long, Sara Mallinson, Richard Morriss, Rob Parker, Adam P Sawczuk, Steven Jones

Bibliographic record

VenueContemporary Clinical Trials · 2015
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsAlberta Health Services
FundersResearch for Patient Benefit ProgrammeNational Institutes of HealthNational Institute for Health and Care ResearchFoundation for the National Institutes of Health
KeywordsPsychological interventionMedicineMoodProtocol (science)Randomized controlled trialMental healthIntervention (counseling)Quality of life (healthcare)Cost effectivenessBipolar disorderPsychiatryNursingRisk analysis (engineering)Alternative medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.030
metaresearch head score (Gemma)0.027
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.375
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0300.027
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.564
GPT teacher head0.605
Teacher spread0.041 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designRandomized trial
Domainnot available
GenreEmpirical

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".

Quick stats

Citations17
Published2015
Admission routes1
Has abstractyes

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