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Record W4401622120 · doi:10.1177/08862605241270047

Adverse Childhood Experiences and Police Contact in Canada

2024· article· en· W4401622120 on OpenAlexaffabout
Alexander Testa, Benjamin Jacobs, Jennifer Thompson, Nelson Pang, Dylan B. Jackson, Jason M. Nagata, Kyle T. Ganson

Bibliographic record

VenueJournal of Interpersonal Violence · 2024
Typearticle
Languageen
FieldPsychology
TopicChild Abuse and Trauma
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCriminal justiceHarassmentCriminologyPsychological interventionPsychologySuicide preventionPoison controlAdverse Childhood ExperiencesMedicinePsychiatryEnvironmental healthMental healthSocial psychology

Abstract

fetched live from OpenAlex

A growing body of research has demonstrated that adverse childhood experiences (ACEs) are a risk factor for criminal justice system contact. However, much of this research is limited by (1) being conducted in the United States and (2) a lack of details on specific types of harmful experiences of criminal justice system contact, such as police contact characterized by intrusion or harassment. Using survey data from 940 individuals aged 16 to 30 in Canada from the Canadian Study of Adolescent Health Behaviors, this study investigates the relationship between ACEs and police contact, focusing on encounters involving intrusion or harassment. Results from logistic and multinomial logistic regression analyses reveal that individuals with high ACE exposure, particularly those with four or more ACEs, are more likely to have police contact, including experiences of intrusion and harassment. The results are significant in understanding the interplay between childhood trauma and later encounters with the criminal justice system, emphasizing the need for trauma-informed approaches in policing and healthcare. The study highlights the importance of early interventions to mitigate the effects of ACEs and prevent adverse outcomes in police interactions.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.573
Threshold uncertainty score0.904

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)0.0010.000

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 head0.009
GPT teacher head0.267
Teacher spread0.259 · 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

Citations1
Published2024
Admission routes2
Has abstractyes

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