Learning About Trauma, Online: What Works and What Is?
Bibliographic record
Abstract
Trauma-informed care guides a growing approach to practice across the field of human services and, as such, increasing efforts have been made to integrate a trauma-informed orientation into post-secondary human service programs. While most approaches to teaching trauma-education are designed for in-person instruction, online training programs are increasingly being employed. However, there are questions about the effectiveness of teaching for this particular topic online. The purpose of this study was to gain a better understanding of the impact of learning about trauma-informed practice online. Specifically, by asking “what works?” and “what is?,” the authors assessed the effectiveness of an online training program, called Being Trauma Aware, to teach about trauma-informed care and prepare post-secondary students for their field of practice. Findings reveal that Being Trauma Aware provides foundational knowledge on trauma-informed practice and develops competence and confidence in future practitioners. The training also increases students’ preparedness for the field, shifting their approach when working with children and youth. Future research can further explore whether online learning facilitates the transfer of knowledge to the field, connecting theory to practice.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".