A Glimpse into the Landscape of Trauma Education in Bachelor of Social Work Programs in Quebec
Bibliographic record
Abstract
Social workers with a bachelor-level education regularly work with trauma-affected populations, yet little is known about the trauma-related training they receive in their generalist education to prepare them to provide adequate trauma support while maintaining their well-being. As such, this research project aims to understand the current state of trauma education in Bachelor of Social Work (BSW) programs in North America, with a particular focus on the local landscape in Quebec. An Appreciative Inquiry-inspired survey was used to assess the presence of trauma education modalities and gather insights from educators on their priorities for enhancing trauma education in their programs.Findings confirm a multi-source interest in trauma education across the academic landscape and reveal the presence of multiple trauma education modalities within BSW curricula, providing insight into the current state of trauma education in Quebec. These findings also highlight the priorities of BSW-level programs’ representatives regarding pathways to enhancing trauma education.The findings of this research may have implications for local curriculum designers, educators interested in enhancing trauma education within their programs. Additionally, this research identifies several areas for further exploration that could guide future research on trauma education at the BSW-level.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.003 |
| Scholarly communication | 0.005 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".