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Record W4384524405 · doi:10.36315/2023inpact083

"SCHOOL ADJUSTMENT OF TEENAGERS: THE RELATIONSHIP BETWEEN BULLYING, VICTIMIZATION AND RESILIENCE FACTORS"

2023· article· en· W4384524405 on OpenAlexaffabout
Nathalie Parent, Safaa Moustadraf

Bibliographic record

VenuePsychological applications and trends · 2023
Typearticle
Languageen
FieldPsychology
TopicBullying, Victimization, and Aggression
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsResilience (materials science)PsychologyDevelopmental psychologyComputer sciencePhysics

Abstract

fetched live from OpenAlex

Bullying and victimization are among the most worrying problems that can undermine school climate. Worldwide, studies show that between 6% and 45% of students are involved in those kinds of behaviours, depending on countries and methodologies In addition to reaching a significant proportion of students, studies list a variety of short-and long-term consequences related to school violence such as various psychological difficulties While school violence is widespread and devastating, some adolescents maintain positive adjustment throughout their schooling despite victimization. Resilience can take a variety of meanings, but it's mostly associated with the ability to maintain normal functioning despite adversity In this context, objectives of the study are: 1) to provide a global picture of bullying and victimization in high schools in Quebec (Canada); 2) to study the relationship between bullying, victimization, resilience factors and school adjustment. A total of 165 high school students completed a survey on bullying, victimization, resilience, and related topics. Results show that 23% of teenagers have adopted bullying behaviours in the past two months, while 31% reported being bullied. Furthermore, 44% of adolescents reported bullying behaviours at some point in their schooling and 61% reported being a victim. Hierarchical regression analysis shows that reported victimization and resilience factors account for 44% of the school adjustment variance, with resilience factors contributing more to the predictive model (26%) than reported victimization (18%). This study highlights the extent of violence in the school context and how resilience components can act as protective factors and maintain positive coping.

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 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 score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.062
GPT teacher head0.359
Teacher spread0.296 · 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.

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

Citations0
Published2023
Admission routes2
Has abstractyes

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