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Record W2988137749 · doi:10.1111/eip.12898

Sleep disturbances in youth at‐risk for serious mental illness

2019· article· en· W2988137749 on OpenAlexaff
Jacqueline Stowkowy, Kali Brummitt, Dominique Bonneville, Benjamin A. Goldstein, JianLi Wang, Sidney H. Kennedy, Signe Bray, Catherine Lebel, Glenda MacQueen, Jean Addington

Bibliographic record

VenueEarly Intervention in Psychiatry · 2019
Typearticle
Languageen
FieldPsychology
TopicSleep and related disorders
Canadian institutionsAlberta Children's HospitalSunnybrook Health Science CentreSt. Michael's HospitalHealth Sciences CentreUniversity Health NetworkUniversity of TorontoHotchkiss Brain InstituteUniversity of OttawaOntario Brain InstituteMental Health Research CanadaChild, Adolescent and Family Mental HealthUniversity of Calgary
Fundersnot available
KeywordsMental illnessAsymptomaticPsychiatrySleep (system call)Pittsburgh Sleep Quality IndexPsychologySleep disorderMedicineDistressClinical psychologyMental healthCognitionSleep qualityInternal medicine

Abstract

fetched live from OpenAlex

AIM: To investigate sleep behaviours of youth at-risk for serious mental illness (SMI). METHODS: This study included 243 youth, ages 12 to 25:42 healthy controls, 41 asymptomatic youth at-risk for mental illness (stage 0); 53 help-seeking youth experiencing distress (stage 1a) and 107 youth with attenuated syndromes (stage 1b). The Pittsburgh Sleep Quality Index was used to assess sleep dysfunction. RESULTS: Stage 1b individuals indicated the greatest deficit in global sleep dysfunction (F = 26.18, P < .0001). Stages 1a and 1b reported significantly worse subjective sleep quality, a longer sleep latency, increased use of sleep medications as well as greater daytime dysfunction compared to the asymptomatic groups. CONCLUSION: Research investigating sleep behaviours of youth considered to be at-risk for SMI is limited. This study provides early evidence that sleep disturbances are worse for individuals considered to be at higher risk of illness development.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.026
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0020.001

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 head0.006
GPT teacher head0.274
Teacher spread0.268 · 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.

Study designObservational
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

Citations16
Published2019
Admission routes1
Has abstractyes

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