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Sexual orientation and experiences with police contact in Canada

2023· article· en· W4389430298 on OpenAlexaffabout
Alexander Testa, Dylan B. Jackson, Juan Del Toro, J’Mag Karbeah, Jason M. Nagata, Kyle T. Ganson

Bibliographic record

VenueAnnals of Epidemiology · 2023
Typearticle
Languageen
FieldPsychology
TopicLGBTQ Health, Identity, and Policy
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHarassmentSexual orientationMedicineLogistic regressionDemographyClinical psychologySocial psychologyPsychologyNursing

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the relationship between sexual orientation and police contact-including police contact with intrusion (i.e., use of intrusive verbal or physical force) and police contact with harassment (i.e., actions making one feel inferior based on appearance, identity, or demographic background)-among a national sample in Canada. METHODS: Logistic and multinomial logistic regression were used to assess the association between sexual orientation and experiences with police contact among a sample of 940 persons ages 16-30 across Canada. RESULTS: Compared to heterosexual participants, persons identifying as bisexual were significantly more likely to report having any police contact in the past 12 months (OR = 1.72, 95% CI = 1.09, 2.70). Bisexual (RRR = 3.45, 95% CI = 1. 83, 6.50) and queer, questioning, and other (RRR = 2.33, 95% CI = 1.15, 4.73) identifying participants were more likely to report having experienced police contact with harassment relative to no police contact, compared to heterosexual individuals. CONCLUSIONS: The current study provides the first analysis of the relationship between sexual minority identity and experiences with adverse police contact in Canada, revealing higher levels of police contact and police contact with harassment, especially among bisexual and queer, questioning, other individuals. Findings suggest that sexual minority persons in Canada experience potentially harmful police contact at elevated rates, which may have significant ramifications for health and traumatic stress responses.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.301

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 integrity0.0000.000
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 head0.199
GPT teacher head0.475
Teacher spread0.276 · 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

Citations4
Published2023
Admission routes2
Has abstractyes

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