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Record W1979596465 · doi:10.12735/ier.v1i2p50

Behaviour Problems and Social Support Which Children Perceived from the Different Sources

2013· article· en· W1979596465 on OpenAlexvenueno aff
Firdevs Savi Çakar

Bibliographic record

VenueInternational Education Research · 2013
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologySocial supportSocial psychologyDevelopmental psychology

Abstract

fetched live from OpenAlex

This research is a descriptive research aimed to examine whether the social support perceived from the different sources is the significant predictor of behaviour problems in children. Participants of the research are composed of 360 students (174 females and 186 males) attending the 5th grades of elementary schools. In the study, Social Support Assessment Scale for Children and Adolescents, and Youth Self Report/YSR 11-18 were used. Data analysis of the study was carried out via descriptive statistics, Mann Whitney U Test and stepwise regression analysis. It was seen in this study that in children, the social support perceived from the family, peer and teachers significantly predicts the behaviour problems. While differentiation in accordance with gender is meaningful in the children’s behaviour problems, it was observed that perceived social support from the different sources of children did not significantly show difference in terms of gender. Consequently, it will be useful for the families and teachers as well as the children in the elementary school period to be made to acquire awareness as regards behaviour problems. Also, the prevention and intervention programs intended for different developmental periods to be developed, for the social support sources to be activated and for healthy coping strategies with behaviour problems to be developed.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.181
Threshold uncertainty score1.000

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)0.0110.001

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 head0.058
GPT teacher head0.381
Teacher spread0.323 · 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; both teacher heads agree on what is shown here.

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

Citations2
Published2013
Admission routes1
Has abstractyes

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