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Record W3178813096

Typologies of child maltreatment and peer bullying victimization, and their associations with adolescent substance use: a latent class analysis

2021· dissertation· en· W3178813096 on OpenAlexfundaboutno aff
Samantha Salmon

Bibliographic record

VenueMspace (University of Manitoba) · 2021
Typedissertation
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsnot available
FundersResearch Manitoba
KeywordsLatent class modelPeer victimizationSubstance usePsychologyClass (philosophy)Developmental psychologyClinical psychologyMedicineHuman factors and ergonomicsPoison controlEnvironmental healthComputer science
DOInot available

Abstract

fetched live from OpenAlex

Every child has the right to live free from violence, yet many children worldwide are victimized in their home and among their peers. Child maltreatment and peer bullying are pervasive problems with serious and lifelong health, educational, and social consequences. Child maltreatment and peer bullying are also associated with substance use in adolescence, which may be used as a maladaptive strategy for coping with adversity. Substance use can be especially harmful during adolescence and may lead to substance use disorders. The purpose of this research was to improve understanding of 1) the patterns and typologies of multiple child maltreatment and peer bullying experiences among adolescents and 2) how the identified victimization typologies are associated with adolescent substance use. The current study used secondary data analysis of the 2014 Ontario Child Health Study. Latent class analysis with a correction procedure for misclassification was conducted to first identify typologies of six types of self-reported child maltreatment and five types of self-reported peer bullying victimization among adolescents aged 14 to 17 years (n = 2,910), and subsequently examine the associations between the identified typologies and self-reported adolescent substance use. For comparison, cumulative victimization exposure was also assessed using logistic regression. All models were first examined unadjusted and then adjusting for parent-reported substance use and sociodemographic characteristics. Findings from this research provide novel information about typologies of childhood victimization and the associations of these typologies with adolescent substance use. Four victimization typologies were identified: Low victimization (76.6%), Violent Home Environment (16.0%), High Verbal/Social Peer Bullying (5.3%), and High Polyvictimization (2.1%). Findings demonstrate that child maltreatment and peer bullying victimization experiences are interrelated and that their co-occurrence is common in the population. Polyvictimization exposure was found to be associated with increased odds of adolescent substance use (adjusted odds ratio range = 2.06 to 3.61). These findings have important implications to help adolescents exposed to polyvictimization. Implementation of trauma- and violence-informed care systems across all child- and adolescent-serving settings, including health, education, justice, and social services sectors, may be a necessary standard of care to protect children’s rights. Public health education on positive parenting practices along with legislated prohibition of corporal punishment is needed as an upstream strategy to prevent violence against children, which may also contribute to reductions in adolescent substance use.

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)0.006
metaresearch head score (Gemma)0.010
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 score0.093
Threshold uncertainty score0.184

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0060.010
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.004
Bibliometrics0.0040.003
Science and technology studies0.0020.002
Scholarly communication0.0040.001
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.000

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 head0.224
Teacher spread0.205 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

Explore more

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