Evaluating a Trauma-Informed Care Training Program for Mental Health Clinicians
Bibliographic record
Abstract
Abstract The aim of this study was to evaluate the interRAI Trauma-Informed Care (TIC) training program based on evidence-informed Collaborative Action Plans. Focus groups and the Attitude Related Trauma-Informed Care (ARTIC) questionnaire addressed clinicians’ and mental health professionals’ attitudes toward the application of TIC with their child and youth clients. An explanatory sequential design was conducted. In total, 105 clinicians and mental health professionals who participated in a 4-hour, in-person or virtual TIC training, two comprehensive seminars, and 28 trauma-informed training web-based modules completed the ARTIC questionnaire. Researchers conducted seven focus groups with clinicians/participants ( N = 23) to discuss the views and effectiveness of the interRAI TIC educational training modules. To quantitatively measure the change of attitudes towards TIC, descriptive statistical analysis was completed using the means and standard deviation of the ARTIC scores at the initial time point, the follow-up time point, and the difference between scores at both time points. Paired sample t-tests were conducted on both the overall score and each of the subscales in each of the three samples (total sample, online subsample, and hybrid subsample). A thematic analysis was conducted to generate qualitative findings from the focus groups. Findings from the quantitative and qualitative analyses suggest that the interRAI TIC training provided clinicians with an improved sense of knowledge and ability to apply trauma-informed care planning with their clients.
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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 | 0.016 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".