A Hazardous Profession: War, Journalists, and Psychopathology
Bibliographic record
Abstract
OBJECTIVE: War journalists often confront situations of extreme danger in their work. Despite this, information on their psychological well-being is lacking. METHOD: The authors used self-report questionnaires to assess 140 war journalists, who recorded symptoms of posttraumatic stress disorder (PTSD) (with the Impact of Event Scale-Revised), depression (with the Beck Depression Inventory-II), and psychological distress (with the 28-item General Health Questionnaire). To control for stresses generic to all journalism, the authors used the same instruments to assess 107 journalists who had never covered war. A second phase of the study involved interviews with one in five journalists from both groups, using the Structured Clinical Interview for Axis I DSM-IV Disorders. RESULTS: The rates of response to the self-report questionnaires were approximately 80% for both groups. There were no demographic differences between groups. Both male and female war journalists had significantly higher weekly alcohol consumption. The war journalists had higher scores on the Impact of Event Scale and the Beck Depression Inventory. Their lifetime prevalence of PTSD was 28.6%, and the rates were 21.4% for major depression and 14.3% for substance abuse. War journalists were not, however, more likely to receive treatment for these disorders. CONCLUSIONS: War journalists have significantly more psychiatric difficulties than journalists who do not report on war. In particular, the lifetime prevalence of PTSD is similar to rates reported for combat veterans, while the rate of major depression exceeds that of the general population. These results, which need replicating, should alert news organizations that significant psychological distress may occur in many war journalists and often goes untreated.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.008 | 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".