Impacts of trauma‐ and violence‐informed care education: A mixed method follow‐up evaluation with health & social service professionals
Bibliographic record
Abstract
OBJECTIVES: Trauma- and violence-informed care (TVIC) creates safety by understanding the impacts of trauma on health and behavior, and the intersecting impacts of structural and interpersonal violence. This study examined the impact, 1-2 years later, of TVIC professional education. DESIGN, SAMPLE AND MEASUREMENTS: We conducted a mixed method descriptive follow-up evaluation (online survey, n = 67, and semi-structured interviews, n = 7) with health and social service providers, leaders and researchers who attended TVIC workshops. Participants were asked how the workshop impacted their thinking, actions and perceptions of organizational changes. RESULTS: Participants reported greater impact on attitudes than on behaviors. The most common change in awareness and thinking related to better understanding of the links among trauma, pain and substance use. Practice changes included more active listening and empathy, less use of jargon and less judgement in care encounters. Participants linked these practices to better care interactions, and more trust, openness and satisfaction among service users. CONCLUSION: Educating health professionals and others (e.g. educators) about trauma, violence, and discrimination is not easy. TVIC education can help shift potentially stigmatizing attitudes which can then precipitate practice change. These approaches are emerging as an important way to improve health and quality of life.
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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 | 0.017 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".