Forced change: Talking trauma — how journalism educators are finding new ways to teach an age-old topic
Bibliographic record
Abstract
What does a "trauma-informed journalist" look like?What are the specific competencies associated with being a trauma-informed journalist?And what are the metrics used to measure the efficacy of current training on trauma-informed approaches to reporting?These questions grow out of a discussion at the Taking Care Roundtable, which brought newsroom leaders, journalism educators, working journalists, union representatives, and other stakeholders together in Ottawa in October 2022 for a two-day meeting intended to surface practical and innovative solutions to address some of the challenges highlighted in Matthew Pearson's and David Seglins's (2022) Taking Care: A report on mental health, well-being and trauma among Canadian media workers.This podcast episode features a discussion among journalism educators about the importance of teaching trauma-informed approaches to reporting, the current gaps in pedagogy and practice, and reflections on the student-led demand for this content in a post-pandemic environment where mental health and well-being is top of mind among many young journalists in training.
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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 | 0.042 | 0.107 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.023 | 0.023 |
| Scholarly communication | 0.032 | 0.024 |
| Open science | 0.004 | 0.014 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.005 | 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".