Trauma and Social Work Field Education
Bibliographic record
Abstract
This chapter explores a Canadian field education team&s;s trauma-informed approach to practicum learning. The team, from the University of Calgary, works within a large faculty located across the province of Alberta, Canada, supporting hundreds of practicums each year, both in person and virtually. The team&s;s approach is focused on creating optimal learning outcomes for bachelor of social work and master of social work students grounded in teaching that is based on ethical, inclusive, trauma-informed practices with individuals, families, organizations, and communities. Applying a trauma-informed lens to social work field education helps the team to respond to the diverse needs and lived experiences of students, including recognizing the impact of trauma on their emerging professional identities and practice skills. This chapter draws on the Substance Abuse and Mental Health Services Administration&s;s four assumptions and six key principles of a trauma-informed approach, including safety, trustworthiness, transparency, peer support, collaboration, mutuality, empowerment, voice, choice, as well as cultural, historical, and gender contexts. This chapter also highlights strategies and practices that utilize a trauma-informed approach to strengthen how field educators teach, mentor, and supervise social work students.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.002 |
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".