Psychological distress in Afghan journalists: a descriptive study
Bibliographic record
Abstract
Purpose Afghanistan is one of the world’s most dangerous places for journalists. There are, however, no data on the mental health of Afghan journalists covering conflict in their country. The study aims to determine the degree to which Afghan journalists are exposed to traumatic events, their perceptions of organizational support, their rates of symptoms of posttraumatic stress disorder (PTSD) and depression, their utilization of mental health services and the effectiveness of the treatment received. Design/methodology/approach The entire study was undertaken in Dari (Farsi). Five major Afghan news organizations representing 104 journalists took part of whom 71 (68%) completed a simple eleven-point analog scale rating perceptions of organizational support. Symptoms of PTSD and depression were recorded with the Impact of Event Scale – Revised (IES-R) and the Centre for Epidemiologic Studies Depression Scale (CES-D), respectively. Behavioral comparisons were undertaken between those journalists who had and had not received mental health therapy. Findings The majority of journalists exceeded cutoff scores for PTSD and major depression and reported high rates for exposure to traumatic events. There were no significant differences in IES-R and CES-D scores between journalists who had and had not received mental health therapy. Most journalists did not view their employers as supportive. Originality/value To the best of authors’ knowledge, this is the first study to collect empirical data on the mental health of Afghan journalists. The results highlight the extreme stressors confronted by them, their correspondingly high levels of psychopathology and the relative ineffectiveness of mental health therapy given to a minority of those in distress. The implications of these findings are discussed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| 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) | 0.002 | 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".