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Screening for childhood mental health problems: outcomes and early identification

2009· article· en· W2069916176 on OpenAlexaff
Marilyn J. Essex, Helena C. Kraemer, Marcia J. Slattery, Linnea R. Burk, W. Thomas Boyce, Hermi Rojahn Woodward, David J. Kupfer

Bibliographic record

VenueJournal of Child Psychology and Psychiatry · 2009
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of British Columbia
FundersNational Institute of Mental Health
KeywordsMental healthIntervention (counseling)PsychologyPublic healthSpecialtyPsychiatryClinical psychologyPsychological interventionMedicineNursing

Abstract

fetched live from OpenAlex

BACKGROUND: Many childhood psychiatric problems are transient. Consequently, screening procedures to accurately identify children with problems unlikely to remit and thus, in need of intervention, are of major public health concern. This study aimed to develop a universal school-based screening procedure based on the answers to three questions: (1) What are the broad patterns of mental health problems from kindergarten to grade 5? (2) What are the grade 5 outcomes of these patterns? (3) How early in school can children likely to develop the most impairing patterns be identified accurately? METHODS: Mothers and teachers reported on a community sample (N = 328) of children's internalizing and externalizing symptoms in kindergarten and grades 1, 3, and 5. In grade 5, teachers reported on children's school-based functional impairments, physical health problems, and service use; mothers reported on children's specialty mental health care. RESULTS: Four patterns distinguished children who (1) never evidenced symptoms; (2) evidenced only isolated symptoms; or evidenced recurrent symptoms, either (3) without or (4) with comorbid internalizing and externalizing. By grade 5, children with recurrent comorbid symptoms had the greatest impairments, physical health problems, and service use. These children can be identified quite accurately by grade 1. CONCLUSIONS: Universal screening at school entry can effectively identify children likely to develop recurrent comorbid symptoms, and would provide a basis for developing optimal targeted intervention programs.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.475
Threshold uncertainty score0.706

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.017
GPT teacher head0.322
Teacher spread0.304 · 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 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

Citations180
Published2009
Admission routes1
Has abstractyes

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