Witnessing images of extreme violence: a psychological study of journalists in the newsroom
Bibliographic record
Abstract
OBJECTIVE: User Generated Content - photos and videos submitted to newsrooms by the public - has become a prominent source of information for news organisations. Journalists working with uncensored material can frequently witness disturbing images for prolonged periods. How this might affect their psychological health is not known and it is the focus of this study. DESIGN: Descriptive, exploratory. SETTING: The newsrooms of three international news organisations. PARTICIPANTS: One hundred and sixteen journalists working with User Generated Content material. MAIN OUTCOME MEASURES: Psychometric data included the re-experiencing, avoidance and autonomic arousal indices of posttraumatic stress disorder (Impact of Event Scale-revised), depression (Beck Depression Inventory-II; BDI-II), a measure of psychological distress (GHQ-28), the latter comprising four subscales measuring somatisation, anxiety, social dysfunction and depression, and mean weekly alcohol consumption divided according to gender. RESULTS: Regression analyses revealed that frequent (i.e. daily) exposure to violent images independently predicted higher scores on all indices of the Impact of Event Scale-revised, the BDI-II and the somatic and anxiety subscales of the GHQ-28. Exposure per shift only predicted scores on the intrusion subscale of the Impact of Event Scale-revised. CONCLUSIONS: The present study, the first of its kind, suggests that frequency rather than duration of exposure to images of graphic violence is more emotionally distressing to journalists working with User Generated Content material. Given that good journalism depends on healthy journalists, news organisations will need to look anew at what can be done to offset the risks inherent in viewing User Generated Content material. Our findings, in need of replication, suggest that reducing the frequency of exposure may be one way to go.
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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 | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".