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Record W4396642028 · doi:10.1186/s13063-024-08116-7

Brief Educational Workshops in Secondary Schools Trial (BESST trial), a school-based cluster randomised controlled trial of the DISCOVER workshop for 16–18-year-olds: recruitment and baseline characteristics

2024· article· en· W4396642028 on OpenAlexfundno aff
Kirsty James, Stephen Lisk, Chloe Payne-Cook, Zamena Farishta, Maria Farrelly, Ayesha Sheikh, Monika Slusarczyk, Sarah Byford, Crispin Day, Jessica Deighton, Claire F. Evans, Peter Fonagy, David Saunders, Irene Sclare, James Shearer, Paul Stallard, Tim Weaver, Jynna Yarrum, Ben Carter, June S. L. Brown

Bibliographic record

VenueTrials · 2024
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
FundersNIHR Maudsley Biomedical Research CentreHealth Technology Assessment internationalKing's College LondonDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsMedicineReferralMoodCluster randomised controlled trialRandomized controlled trialEthnic groupFeelingFamily medicineDepression (economics)Cluster (spacecraft)Clinical trialMental healthPsychological interventionClinical psychologyPsychiatryPsychologySocial psychology

Abstract

fetched live from OpenAlex

BACKGROUND: The Brief Educational Workshops in Secondary Schools Trial (BESST) is an England-wide school-based cluster randomised controlled trial assessing the clinical and cost-effectiveness of an open-access psychological workshop programme (DISCOVER) for 16-18-year-olds. This baseline paper describes the self-referral and other recruitment processes used in this study and the baseline characteristics of the enrolled schools and participants. METHOD: We enrolled 900 participants from 57 Secondary schools across England from 4th October 2021 to 10th November 2022. Schools were randomised to receive either the DISCOVER day-long Stress workshop or treatment as usual which included signposting information. Participants will be followed up for 6 months with outcome data collection at baseline, 3-month, and 6-month post randomisation. RESULTS: Schools were recruited from a geographically and ethnically diverse sample across England. To reduce stigma, students were invited to self-refer into the study if they wanted help for stress. Their mean age was 17.2 (SD = 0.6), 641 (71%) were female and 411 (45.6%) were from ethnic minority groups. The general wellbeing of our sample measured using the Mood and Feelings Questionnaire (MFQ) found 314 (35%) of students exhibited symptoms of depression at baseline. Eighty percent of students reported low wellbeing on the Warwick Edinburgh Mental Wellbeing Scale (WEMWBS) suggesting that although the overall sample mean is below the cut-off for depression, the self-referral approach used in this study supports distressed students in coming forward. CONCLUSION: The BESST study will continue to follow up participants to collect outcome data and results will be analysed once all the data have been collected. TRIAL REGISTRATION: ISRCTN registry ISRCTN90912799. Registered on 28 May 2020.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.011
Threshold uncertainty score0.036

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.096
GPT teacher head0.389
Teacher spread0.292 · 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 source (direct Gemma or distilled Codex), 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

Citations1
Published2024
Admission routes1
Has abstractyes

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