Impacts of trauma‐ and violence‐informed care education: A mixed method follow‐up evaluation with health & social service professionals
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it