Designing a trauma informed service to deliver trauma therapy with people experiencing homelessness: a qualitative study
Bibliographic record
Abstract
BACKGROUND: People who are homeless experience an increased prevalence of traumatic events, including childhood trauma, trauma related to being homeless, and structural trauma. It is important to consider trauma in the delivery of health services for this population. Using a trauma-informed care approach is one way to ensure that a service or program takes into consideration the effects of trauma. The aims of this study are to describe how best to design a service to engage people experiencing homelessness in a trauma-focused therapy as well as detail what trauma-informed care would look like in this setting. METHODS: We conducted a series of qualitative interviews about how to design a trauma-informed trauma therapy for people experiencing homelessness and their perspectives on different principles of trauma-informed care. Thematic analysis was used to identify, analyze and report themes identified in the data. RESULTS: We conducted 12 in-depth interviews (8 women, 4 men) with people who were currently peer support workers with lived experience of trauma and homelessness. We identified themes to design a trauma-informed service including low-barrier access, communication strategies, meeting people's needs, and how to engage and retain people in the service. We also identified themes related to how people with lived experience understand the principles of trauma informed care. DISCUSSION: The findings from this study provide insight and practical recommendations for designing and implementing a trauma-informed therapy tailored for people experiencing homelessness. The findings here shed light on the lived experience perspective of trauma-informed care principles, adding nuance to our understanding of what it means to be trauma-informed.
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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 | 0.020 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.015 | 0.014 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".