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Record W3021874360 · doi:10.1093/pch/pxx198

Screening for disruptive behaviour problems in preschool children in primary health care settings

2018· erratum· en· W3021874360 on OpenAlexaff
Alice Charach, Stacey A Bélanger, John D. McLennan, Mary K. Nixon

Bibliographic record

VenuePaediatrics & Child Health · 2018
Typeerratum
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsCanadian Paediatric Society
Fundersnot available
KeywordsPrimary carePrimary health careMedicinePrimary (astronomy)PsychologyDevelopmental psychologyFamily medicinePediatricsEnvironmental health

Abstract

fetched live from OpenAlex

Disruptive behaviour problems in preschool children are significant risk factors for, and potential components of, neurodevelopmental and mental health disorders. Some non-compliance, temper tantrums and aggression between two and five years of age are normal and transient. However, problematic levels of disruptive behaviour, specifically when accompanied by functional impairment and/or significant distress, should be identified because early intervention can improve outcome trajectories. This position statement provides an approach to early identification using clinical screening at periodic health examinations, followed by a systematic mental health examination that includes standardized measures. The practitioner should consider a range of environmental, developmental, family and parent-child relationship factors to evaluate the clinical significance of disruptive behaviours. Options within a management plan include regular monitoring accompanied by health guidance and parenting advice, referral to parent behaviour training as a core evidence-based intervention, and referral to specialty care for preschool children with significant disruptive behaviours, developmental or mental health comorbidities, or who are not responding to first-line interventions.

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)0.002
metaresearch head score (Gemma)0.017
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.041
Threshold uncertainty score0.081

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.017
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.003

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 head0.016
GPT teacher head0.302
Teacher spread0.287 · 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

Citations10
Published2018
Admission routes1
Has abstractyes

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