The psychological effects of reporting extreme violence: a study of Kenyan journalists
Bibliographic record
Abstract
OBJECTIVE: To assess the psychological health of journalists in Kenya who have reported on, and been exposed to, extreme violence. DESIGN: Descriptive. Psychological responses were elicited to two stressors, the ethnic violence surrounding the disputed 2007 general election and the Al-Shabab attack on the Westgate Mall in Nairobi. PARTICIPANTS: A representative sample of 90 Kenyan journalists was enrolled. SETTING: Newsrooms of two national news organizations in Kenya. MAIN OUTCOME MEASURES: Symptoms of post-traumatic stress disorder (Impact of Event Scale-revised), depression (Deck Depression inventory-revised) and general psychological wellbeing (General Health Questionnaire). RESULTS: Of the 90 journalists approached 57 (63.3%) responded. Journalists covering the election violence (n = 23) reported significantly more PTSD type intrusion (p = 0.027) and arousal (p = 0.024) symptoms than their colleagues (n = 34) who had not covered the violence. Reporting the Westgate attack was not associated with increased psychopathology. Being wounded (n = 11) emerged as the most robust independent predictor of emotional distress. Journalists covering the ethnic violence compared to colleagues who did not were not more likely to receive psychological counselling. CONCLUSIONS: These data, the first of their kind from an African country, replicate findings over a decade old from Western media, namely that journalists asked to cover life-threatening events may develop significant symptoms of emotional difficulties and fail to receive therapy for them. Good journalism, a pillar of civil society, depends on healthy journalists. It is hoped that these data act as a catalyst encouraging news organisations sending journalists into harm's way to look out for their psychological health in doing so.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".