Trauma-informed care beliefs scale-comprehensive for child welfare carers using Rasch analysis
Bibliographic record
Abstract
BACKGROUND: The literature on trauma-informed care practices (TIC) indicates that this framework is beneficial for young people, carers, and staff. However, a significant gap in the literature and practice is the absence of psychometrically sound scales to measure carer adherence to TIC principles. Emerging evidence suggests that TIC practices shift carer attitudes and beliefs, which mediate positive outcomes for both carers and young people. OBJECTIVE: To develop a theoretically comprehensive and psychometrically sound measure of carer TIC beliefs using Rasch methodology. PARTICIPANTS AND SETTING: Active carers (N = 719, M = 43 years, SD = 10.7 years) from online support groups in Australia, Canada, the United States of America, the United Kingdom, and the Republic of Ireland completed the questionnaire online. METHODS: Based on previous research (e.g., limitations of the Trauma-Informed Belief Scale-Brief [TIBS-B]; Beehag, Dryer, et al., 2023a) and a scoping review of the TIC literature (Beehag, 2023), 61 candidate items were created that covered the three main characteristics of carer-related TIC theory (i.e., beliefs on TIC strategies to manage trauma symptoms, beliefs on the impact of adverse childhood experiences (ACE), and beliefs on the importance of self-care/reflection). The resulting data was subjected to Rasch analyses. RESULTS: Following analyses and minor modifications, a 35-item version of the questionnaire was confirmed, which fitted the Rasch model and demonstrated unidimensionality, reasonable targeting, and sound internal consistency reliability (Person Separation Index = 0.81). CONCLUSIONS: The TIBS-C is a psychometrically sound measure of child welfare carer TIC beliefs. Future studies are needed to provide further evidence of its validity (e.g., predictive validity), reliability (e.g., test-retest reliability) and clinical utility.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".