The psychometric properties of childhood physical and sexual abuse measures in two Canadian samples of youth and emerging adults
Bibliographic record
Abstract
INTRODUCTION: Child maltreatment is prevalent in Canada; how we measure it varies. The objective of the current study was to examine the psychometric properties of the Childhood Experiences of Violence Questionnaire Short Form (CEVQ-SF) physical and sexual abuse measures and of the Canadian Community Health Survey (CCHS) 2-item sexual abuse measure, compared with the Childhood Trauma Questionnaire (CTQ) in two samples of adolescents and young adults. METHODS: Retrospective, self-reported child abuse history was collected in the British Columbia Healthy Connections Project (BCHCP) and in the Well-Being and Experiences (WE) Study. Internal consistency, criterion validity, and construct validity were examined. RESULTS: Across both samples, the prevalence of child physical abuse (CPA) and child sexual abuse (CSA) ranged from 12.5% to 41.4% and from 5.8% to 34.3%, respectively. Internal consistencies were good-to-acceptable for CPA using the CEVQ-SF in the BCHCP (α = 0.83) and the WE Study (α = 0.79) and for CSA using the CEVQ-SF in the WE Study (α = 0.68). For CPA, in both studies, the highest agreement-moderate-to-fair-was between CEVQ-SF severe CPA and CTQ moderate CPA: κ=0.63 (BCHCP) and κ= 0.35 (WE Study). For CSA, agreement with CTQ moderate cut-offs was substantial in the BCHCP (κ=0.77) and fair in the WE Study (κ=0.37). DISCUSSION: Our findings support current and future use of the CEVQ-SF for CPA, and for CSA, using both the CEVQ-SF and the CCHS-CSA measure, given that they had good psychometric properties when administered to two samples of adolescents and young adults.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".