Simulated solutions: Using a clinical simulation exercise to prepare journalism students for trauma-intensive interviews
Bibliographic record
Abstract
When disaster strikes, journalists are often among the first on scene.They are also there in the aftermath, speaking to survivors as they come to terms with what has happened to them.How journalists interact with and interview trauma survivors without causing further harm has increasingly become a focus of newsrooms and, by extension, the journalism programs whose mission it is to train students to enter the industry.Yet despite research on the impacts journalists can suffer as a result of covering traumatic events, training on trauma-informed approaches to interviews is limited.Drawing on the use of clinical simulations in higher education classroom environments, this article outlines how an interview simulation exercise was conceived and conducted as part of a specialized course on trauma-informed reporting at a university in Ottawa, Ontario, Canada.Included are insights from students who participated in the simulation exercise and considerations of where simulation exercises might elsewhere be used in a journalism-training context.The widespread adoption of video conferencing tools as part of the shift to online learning during the COVID-19 pandemic, which imposed changes to long-established pedagogies, facilitated the use of such tools to conduct the outlined interview simulation exercise in an accessible, innovative, and practical manner.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.007 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".