CREATING EVIDENCE-BASED CHANGE THROUGH A TRAUMA-INFORMED LENS: TRANSLATING PRINCIPLES INTO PRACTICE
Bibliographic record
Abstract
When programs and services incorporate an understanding of trauma and its impact on an individual’s behaviour and ability to cope, the potential for misdiagnosis and inadequate treatment planning is significantly reduced. Incorporating trauma-informed approaches into service delivery is an essential component to developing programs that accurately address the needs of youth and their families. The organization involved in this study, in the Province of British Columbia, Canada, provides an extensive array of services to youths aged 12 to 18 years who have significant emotional, behavioural, and psychiatric difficulties. In a joint multidisciplinary effort to better support traumatized young people and their families, the organization embarked on an in-depth evaluation of its service delivery. Together the team co-created a shift in practice that supported the translation of trauma-informed principles into practice and developed valid and measurable methods for evaluation through the adoption of a participatory action framework. Four semi-structured interviews were developed for collecting qualitative feedback from clients, stakeholders, and staff who experienced the change in service delivery across 5 clinical cases over the course of 8 months. The feedback confirmed that the shift in practice was effective in cultivating an environment of safety, choice, and collaboration for clients. This resulted in the development of an evidence-based shift in service delivery as well as identifying training needs and developing plans to integrate this change into broader practice throughout the organization.
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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 | 0.309 | 0.287 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.016 | 0.007 |
| Science and technology studies | 0.010 | 0.053 |
| Scholarly communication | 0.033 | 0.023 |
| Open science | 0.010 | 0.023 |
| Research integrity | 0.012 | 0.022 |
| 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".