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Childhood adversities and their associations with mental disorders in the World Mental Health International College Student surveys initiative

2025· article· en· W4411193670 on OpenAlexafffund
Mathilde M. Husky, Sue Lee, Nancy A. Sampson, Shelby Borowski, Yesica Albor, Ahmad N. AlHadi, Jordi Alonso, Nouf Al-Saud, Yasmin Altwaijri, Claes Andersson, Lukoye Atwoli, Caroline Ayuya Muaka, Patricia M Báez-Mansur, Laura Ballester, Jason Bantjes, Harald Baumeister, Marcus Bendtsen, Corina Benjet, Anne H. Berman, Ronny Bruffaerts, Paula Carrasco, Silver C N Chan, Irina Cohut, María Anabell Covarrubias Díaz Couder, Paula Cristóbal-Narváez, Marcelo A. Crockett, Pim Cuijpers, Daniel David, Dong Dong, David Daniel Ebert, Carlos G. Forero, Jorge Gaete, Margalida Gili, Raúl A. Gutiérrez–García, Josep María Haro, Penelope Hasking, Xanthe Hunt, Florence Jaguga, Leontien Jansen, Álvaro I. Langer, Irene Léniz, Yan Liu, Christine Löchner, Scarlett Mac‐Ginty, Vania Martínez, Andre Mason, Muthoni Mathai, Margaret McLafferty, Elaine Murray, Catherine Mawia Musyoka, Cătălin Nedelcea, Daniel Núñez, Siobhan O’Neill, José A. Piqueras, Codruța Alina Popescu, Charlene Rapsey, Kealagh Robinson, Tíscar Rodríguez‐Jiménez, Wylene Saal, Oi Ling Siu, Dan J. Stein, Sascha Y. Struijs, Cristina Tomoiagă, Karla Patricia Valdés‐García, Eunice Vargas‐Contreras, Shelby Vereecke, Daniel Vigo, Angel Y Wang, Samuel Yeung Shan Wong, Ronald C. Kessler

Bibliographic record

VenuePsychiatry Research · 2025
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of British Columbia
FundersOrtho-McNeil PharmaceuticalFeilman FoundationInterregDivision of Research Capacity DevelopmentNational Health and Medical Research CouncilInstituto de Salud Carlos IIIPfizer FoundationH. Lundbeck A/SForeign, Commonwealth and Development OfficeAgència de Gestió d'Ajuts Universitaris i de RecercaNational Research FoundationVlaamse regeringMedical Research CouncilServierFolkhälsomyndighetenSaudi Basic Industries CorporationNational Institute of Mental HealthHealth CanadaMinisterul Cercetării, Inovării şi DigitalizăriiKoning BoudewijnstichtingEli Lilly and CompanyCentro de Investigación Biomédica en Red de Salud MentalUniversity of British ColumbiaKing Abdulaziz City for Science and TechnologyUnitatea Executiva pentru Finantarea Invatamantului Superior, a Cercetarii, Dezvoltarii si InovariiVetenskapsrådetMinisterio de Ciencia e InnovaciónUNICEFConsejo Nacional de Ciencia y TecnologíaAgencia Nacional de Investigación y DesarrolloTaishan Scholar Foundation of Shandong ProvinceGeneralitat de CatalunyaVistagen TherapeuticsKing Saud UniversityInstitut Universitaire de FranceKing Faisal Specialist Hospital and Research CentreMinistry of Health – Kingdom of Saudi ArabiaAfrican UnionZonMwFogarty International CenterNational Institutes of HealthVolkswagen FoundationSouth African Medical Research CouncilGovernment of the United KingdomSvenska Forskningsrådet FormasProvincial Health Services AuthoritySuicide Prevention AustraliaPan American Health OrganizationKing Salman Center for Disability ResearchPfizerChild Mind InstituteWellcome TrustU.S. Public Health ServiceSage TherapeuticsMassachusetts General HospitalPublic Health AgencyGlaxoSmithKlineNational Institute on Drug AbuseJohn D. and Catherine T. MacArthur FoundationBristol-Myers SquibbEuropean CommissionSanofi
KeywordsMental healthPsychologyPsychiatryClinical psychology

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 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)0.002
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.063
Threshold uncertainty score0.126

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.037
GPT teacher head0.402
Teacher spread0.365 · 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 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

Citations6
Published2025
Admission routes2
Has abstractno

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