Teaching the art of compassionate inquiry: involving survivors from 9/11 in social work education
Bibliographic record
Abstract
This paper reports findings from pedagogic research evaluating the impact of the involvement of survivors from the World Trade Center attacks in New York City in 2001 in trauma-specific social work education. A pedagogic approach to mental health education is discussed which aims to prepare students to develop trauma-informed assessment and intervention skills concurrent with their encounters with trauma survivors in field practice placements. The small-scale research involved surveying students’ evaluations at a university in New York, following exposure to first-hand accounts of survivors’ experiences. Across the three areas—confidence in knowledge of trauma, impact on learning, and preparation for field practice—the evaluation findings indicate that the students’ knowledge, gained from the involvement of 9/11 survivors, improved over time. This paper presents the background to this project, preparations involved, and findings from research evaluations with the students. The findings suggest that the involvement of those with direct and lived trauma experience in classroom teaching, whilst challenging, can yield positive impacts for students. The 9/11 survivors poignantly shared with students that their lives were changed forever in the aftermath of these events. The findings have potential global educational impact and resonance.
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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 | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.002 | 0.003 |
| 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".