Impacts of child welfare worker and clientele characteristics on attitudes toward trauma informed-care
Bibliographic record
Abstract
Background: There is increasing recognition of the need to integrate trauma-informed care (TIC) into child welfare practices, given the high rates of trauma experiences among children and youth across these settings. The implementation of TIC is facilitated by various elements, including worker attitudes, yet further research is needed to illuminate the factors that influence child welfare workers’ positive regard for TIC. Objectives: This study aims to explore the relationship between child welfare worker attitudes regarding TIC with workers’ and clients’ individual characteristics. Methods: N = 418 child welfare workers from 11 agencies completed two measures: a demographic questionnaire as well as the French translated version of the ARTIC-35 questionnaire comprised of five subscales. Linear mixed effects models were run for each ARTIC subscale, examining how child and worker factors affect attitudes toward TIC. Results: Participants indicated relatively positive attitudes toward TIC. Managerial staff in offender units scored higher on the subscale regarding their beliefs about the causes underlying child behaviors and on the subscale regarding beliefs about the secondary effects of trauma, than their counterparts in protection units serving boys. Managers scored higher than frontline staff on worker self-efficacy, response to problem behavior, and on-the-job behavior subscales. Workers with a community college degree—and not a university degree–indicated greater sense of self-efficacy. Conclusions: This study points to the importance of paying attention to the characteristics of both workers and clients that may influence inclination toward TIC principles, as a means to build effective integration of this approach in child-serving settings.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".