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Record W1770929042 · doi:10.1002/imhj.21402

Hyperactive Behaviors Among 17‐Month‐Olds in a Population‐Based Cohort

2013· article· en· W1770929042 on OpenAlexaff
Elisa Romano, Raymond H. Baillargeon, Guanqiong Cao

Bibliographic record

VenueInfant Mental Health Journal · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsPsychologyAggressionDevelopmental psychologyNormativeLatent class modelPopulationClinical psychologyDemography

Abstract

fetched live from OpenAlex

ABSTRACT During a child's early years, it may be difficult to distinguish between problematic versus normative heightened motor activity. This distinction is important because disorganized heightened motor activity (often labeled hyperactive behavior ) can interfere with early childhood developmental tasks. The current study used latent class analyses to estimate the proportion of 17‐month‐olds in the general population who exhibit hyperactive behaviors on a frequent basis (testing for sex differences) and to examine the extent to which toddlers with frequent hyperactive behaviors also might be frequently exhibiting other problem behaviors. We used mother‐reported cross‐sectional data on 2,045 toddlers from a provincially representative study. Results indicated that it is possible to distinguish between 17‐month‐olds who exhibit hyperactive behaviors on a frequent basis and those who never or only occasionally exhibit such behaviors. Specifically, 28.1% of toddlers exhibited hyperactive behaviors on a frequent basis. There were no significant sex differences in the probability of belonging to a particular latent class, but boys in the high‐hyperactivity latent class had a greater propensity to exhibit hyperactive behaviors on a frequent basis. While hyperactivity was highly correlated with both physical aggression and opposition‐defiance, it appears to already represent a distinct behavior problem in young children.

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 score0.028
Threshold uncertainty score0.997

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)0.0030.000

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 head0.016
GPT teacher head0.324
Teacher spread0.308 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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