The Preliminary Development and Validation of a Trauma‐Related Safety‐Seeking Behavior Measure for Youth: The Child Safety Behavior Scale (CSBS)
Bibliographic record
Abstract
Safety-seeking behaviors (SSBs) may be employed after exposure to a traumatic event in an effort to prevent a feared outcome. Cognitive models of posttraumatic stress disorder propose SSBs contribute to maintaining this disorder by preventing disconfirmation of maladaptive beliefs and preserving a sense of current threat. Recent research has found that SSBs impact children's posttraumatic stress symptoms (PTSS) and recovery. In this paper, we sought to develop and validate a novel 22-item Child Safety Behavior Scale (CSBS) in a school-based sample of 391 pupils (age 12-15 years) who completed a battery of questionnaires as well as 68 youths (age 8-17 years) who were recently exposed to a trauma. Of the sample, 93.1% (N = 426) completed the new questionnaire. The sample was split (n = 213), and we utilized principal components analysis alongside parallel analysis, which revealed that 13 items loaded well onto a two-factor structure. This structure was superior to a one-factor model and overall demonstrated a moderately good model of fit across indices, based upon a confirmatory factory analysis with the other half of the sample. The CSBS showed excellent internal consistency, r = .90; good test-retest reliability, r = .64; and good discriminant validity and specificity. In a multiple linear regression, SSBs, negative appraisals, and number of trauma types each accounted for unique variance in a model of PTSS. This study provides initial support for the use of the CSBS in trauma-exposed youth as a valuable tool for further research, clinical assessment, and targeted intervention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".