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Record W2065688916 · doi:10.1177/0143034313515984

Conduct problems in young, school-going children in Ireland: Prevalence and teacher response

2013· article· en· W2065688916 on OpenAlexaboutno aff
Lynda Hyland, G. Ni Mhaille, Anne Lodge, Sinéad McGilloway

Bibliographic record

VenueSchool Psychology International · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicEarly Childhood Education and Development
Canadian institutionsnot available
Fundersnot available
KeywordsQuarter (Canadian coin)Intervention (counseling)PsychologyPerceptionClassroom managementDevelopmental psychologyClass (philosophy)Medical educationMathematics educationMedicinePsychiatry

Abstract

fetched live from OpenAlex

Conduct problems in school settings can pose significant challenges for both children and teachers. This study examined the teacher-reported prevalence of conduct problems in a sample of young children ( N = 445) in the first two years of formal education. A secondary aim was to assess teachers’ perceptions of child behaviour and their classroom management strategies. The study was undertaken in 11 schools located in south west Ireland. Overall, children displayed positive socio-emotional and behavioural adjustment, although more than one-quarter had difficulties outside the ‘normal’ range. Class size and gender were shown to play a role in the level of difficulties experienced. Teachers reported significant challenges in managing classroom behavioural problems. This study provides some useful insights into the socio-emotional and behavioural needs of school-entry age children. The findings also have important policy and practice implications for school psychologists and other key school personnel and highlight, in particular, the need to develop and implement early intervention and prevention strategies in schools.

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)0.001
metaresearch head score (Gemma)0.003
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 score0.058
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.357
Teacher spread0.333 · 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

Citations18
Published2013
Admission routes1
Has abstractyes

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