2SLGBTQ+ competent trauma-informed care: A mixed methods evaluation of an intervention to enhance the capacity of multidisciplinary service providers.
Bibliographic record
Abstract
OBJECTIVE: Given that two-spirit, lesbian, gay, bisexual, transgender, and queer (2SLGBTQ+) people are more likely experience trauma and gender-based violence in their lifetimes compared with heterosexual and/or cisgender peers, it is important that service providers addressing violence and/or trauma be 2SLGBTQ+ culturally competent to support 2SLGBTQ+ communities and avoid retraumatization. To address this need, we developed and implemented a novel 2SLGBTQ+ competent trauma-informed care (TIC) intervention across Ontario, Canada. This article evaluates the intervention based on learning outcomes, professional relevance, changes in knowledge and comfort, and impacts on individual and organizational practices. METHOD: We used mixed methods to assess the acceptability and impact of the intervention on multidisciplinary service providers. The intervention was evaluated with principles of the Kirkpatrick model. Pre- to postsurveys were used to quantitatively assess the intervention's acceptability, as well as comfort and knowledge regarding 2SLGBTQ+ competent TIC. Qualitative interviews were conducted to understand acceptability and impact on personal, professional practice, and organizational levels. RESULTS: = 62). Interviews were conducted with 20 participants. Quantitative surveys indicated an increase in knowledge and comfort in providing TIC for 2SLGBTQ+ people. Qualitative interviews indicated a multilevel impact on service providers' capacity and delivery of 2SLGBTQ+ competent TIC. CONCLUSION: We found that novel interventions to engage service providers in providing 2SLGBTQ+ competent TIC are impactful and merit further development. (PsycInfo Database Record (c) 2026 APA, all rights reserved).
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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 | 0.010 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 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) | 0.005 | 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".