Factor Structure and Reliability of the Childhood Trauma Questionnaire and Prevalence Estimates of Trauma for Male and Female Street Youth
Bibliographic record
Abstract
This study examines the psychometric properties of the Childhood Trauma Questionnaire short form (CTQ-SF) with street youth who have run away or been expelled from their homes (N = 397). Internal reliability coefficients for the five clinical scales ranged from .65 to .95. Confirmatory Factor Analysis (CFA) was used to test the five-factor structure of the scales yielding acceptable fit for the total sample. Additional multigroup analyses were performed to consider items by gender. Results provided only evidence of weak factorial invariance. Constrained models showed invariance in configuration, factor loadings, and factor covariances but failed for equality of intercepts. Mean trauma scores for street youth tended to fall in the moderate to severe range on all abuse/neglect clinical scales. Females reported higher levels of abuse and neglect. Prevalence of child maltreatment of individual forms was very high with 98% of street youth reporting one or more forms; 27.4% of males and 48.9% of females reported all five forms. Results of this study support the viability of the CTQ-SF for screening maltreatment in a highly vulnerable street population. Caution is recommended when comparing prevalence estimates for male and female street youth given the failure of the strong factorial multigroup model.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".