Civil War in Syria: the psychological effects on journalists
Bibliographic record
Abstract
Purpose – More journalists died in Syria during 2013 than in any other country experiencing conflict. This statistic raises concerns about the psychological wellbeing of journalists covering the internecine violence. The paper aims to discuss this issue. Design/methodology/approach – The study sample was made up of 59 western journalists currently covering the Syrian conflict. To place these results in the broader context of war journalism previously collected data from a group of 84 journalists who had reported the war in Iraq were used as a control sample. Outcome measures included indices of posttraumatic stress disorder (PTSD) (Impact of Event Scale-revised) and psychological distress (General Health Questionnaire-28 item version (GHQ-28)). Findings – Compared to journalists who covered the Iraq war, the journalists working in Syria were more likely to be female (p=0.007), single (p=0.018), freelance (p=0.0001) and had worked fewer years as a journalist (p=0.012). They were more depressed according to the GHQ-28 (p=0.001) and endorsed more individual symptoms of depression including worthlessness (p=0.012), helplessness (p=0.02) and suicidal intent (p=0.003). A linear regression analysis revealed that the group differences in depression data could not be accounted for by demographic factors. Research limitations/implications – An absence of structured interviews. Results not applicable to local Syrian journalists. Practical implications – Western journalists covering Syrian appear to be particularly vulnerable to the development of depression. Journalists and the news organizations that employ them need to be cognizant of data such as these. Given that depression is treatable, there needs to be a mechanism in place to detect and treat those in need. Originality/value – This is the first study that highlights the emotional toll on western journalists covering the Syrian conflict.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".