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Record W2023201539 · doi:10.1177/082957350001600102

The Effect of School Culture on Adolescent Behavioural Problems: SelfEsteem, Attachment to Learning, and Peer Approval of Deviance as Mediating Mechanisms

2000· article· en· W2023201539 on OpenAlexaffabout
David J. DeWit, David R. Offord, Mark Sanford, Barbara J. Rye, Martin Shain, Robin Wright

Bibliographic record

VenueCanadian Journal of School Psychology · 2000
Typearticle
Languageen
FieldPsychology
TopicChild and Adolescent Psychosocial and Emotional Development
Canadian institutionsMcGill UniversitySt. Jerome's UniversityMcMaster UniversityCentre for Addiction and Mental Health
Fundersnot available
KeywordsPsychologyDeviance (statistics)Developmental psychologyAutonomyDisciplineAcademic achievementStructural equation modelingSocial psychologyClinical psychology

Abstract

fetched live from OpenAlex

Adopting a social ecological perspective, this study examined the role of adolescent personality and social traits as mediating mechanisms linking school culture with adolescent behavioural problems. Data were obtained from the self-reports of 1,100 grade nine students attending four southern Ontario high schools. Structural equation model results revealed that student exposure to an unfavourable school culture (marked by perceptions of low teacher and classmate support, student conflict, unfair school rules and disciplinary practices, and low student autonomy in school decision-making and affairs) was positively associated with low attachment to learning and peer approval of deviance each of which were positively associated with disciplinary problems, conduct disorder, oppositional-defiant disorder, attention-deficit hyperactivity, and substance use. Low self-esteem emerged as a significant mediator in two models. Significant direct effects of school culture were found for most outcomes. Discussion focused on the significance of study findings for future program development and research.

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 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 score0.119
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.019
GPT teacher head0.313
Teacher spread0.294 · 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

Citations44
Published2000
Admission routes2
Has abstractyes

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