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Record W4280573135 · doi:10.1016/s2215-0366(22)00103-1

The EMPOWER blended digital intervention for relapse prevention in schizophrenia: a feasibility cluster randomised controlled trial in Scotland and Australia

2022· article· en· W4280573135 on OpenAlexaff
Andrew Gumley, Simon Bradstreet, John Ainsworth, Stephanie Allan, Mario Álvarez‐Jiménez, Lorna Aucott, Max Birchwood, Andrew Briggs, Sandra Bucci, Sue Cotton, Lidia Engel, Paul French, Reeva Lederman, Shôn Lewis, Matthew Machin, Graeme MacLennan, Hamish J. McLeod, Nicola McMeekin, Emma Morton, John Norrie, Matthias Schwannauer, Swaran P. Singh, Suresh Sundram, Andrew Thompson, Chris Williams, Alison R. Yung, John Farhall, John Gleeson

Bibliographic record

VenueThe Lancet Psychiatry · 2022
Typearticle
Languageen
FieldPsychology
TopicDigital Mental Health Interventions
Canadian institutionsUniversity of British Columbia
FundersNational Health and Medical Research CouncilAustralian Research CouncilMedical Research CouncilHealth Technology Assessment ProgrammeEconomic and Social Research CouncilUniversity of MelbourneChief Scientist OfficeUniversity of GlasgowDepartment of Health and Social CareAlan Turing InstituteScottish GovernmentEngineering and Physical Sciences Research CouncilNational Institute for Health and Care ResearchNHS Health Scotland
KeywordsMedicineRandomized controlled trialCluster randomised controlled trialIntervention (counseling)TriageRelapse preventionMental healthPsychiatryFamily medicinePsychology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.484

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.047
GPT teacher head0.390
Teacher spread0.343 · 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; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations72
Published2022
Admission routes1
Has abstractno

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