Expanding the Trauma-Informed Care Measurement Toolkit: An Evaluation of the Attitudes Related to Trauma-Informed Care (ARTIC-45) Scale with SUD Workers in PIMH
Bibliographic record
Abstract
Human service organizations (HSO) have increasingly recognized the value of employing trauma-informed care (TIC) in a variety of practice settings. Evidence suggests that effectively adopting TIC has shown client improvements. Organizational barriers to TIC implementation, however, exist. To improve TIC practice, the attitudes related to trauma-informed care (ARTIC) scale was developed to measure staff attitudes and beliefs towards TIC. The ARTIC has been widely adopted by researchers without evaluating its psychometric performance in diverse practice settings. The purpose of this study was to independently validate the ARTIC scale drawn from a sample of staff (n = 373) who provide services to substance-using parents. Psychometric tests were conducted to evaluate how the ARTIC performs with our HSO population. Results from a confirmatory factor analysis showed poor fit (X2 = 2761.62, df = 2.96; RMSEA = 0.07 [0.07, 0.08]; CFI = 0.72). An exploratory factor analysis was conducted to analyze how the data fit with our specific population, yielding 10 factors. Finally, a qualitative inter-item analysis of these factors was conducted, resulting in nine factors. Our findings suggest that measuring TIC attitudes and beliefs may vary according to field of practice and ethno-racially diverse workers. Further refinement of the ARTIC may be necessary for various services domains.
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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 | 0.032 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".