How have journalists been affected psychologically by their coverage of the COVID-19 pandemic? A descriptive study of two international news organisations
Bibliographic record
Abstract
OBJECTIVES: The COVID-19 pandemic has presented unprecedented healthcare challenges. Journalists covering the pandemic at close quarters are working in ways akin to first responders, but nothing to date is known of the psychological distress this is potentially causing them. This study aims to determine whether journalists reporting on the COVID-19 crisis have been affected emotionally, and if so to assess the severity of their distress. It also investigates potential demographic and work-related predictors and whether news organisations had provided counselling to their journalists. PARTICIPANTS: A total of 111 journalists working for two international news organisations were approached of which 73 (66%) participated in the study. PRIMARY AND SECONDARY OUTCOME MEASURES: Symptoms of anxiety (Generalised Anxiety Disorder Scale-7 (GAD-7)), depression (Patient Health Questionnaire (PHQ-9)), posttraumatic stress disorder (PTSD; PTSD Checklist for DSM-5 (PCL-5)), overall psychological distress (12-item General Health Questionnaire (GHQ-12)), and treatment. RESULTS: The percentages of journalists exceeding threshold scores for clinically significant anxiety, depression, PTSD and psychological distress were: GAD-7, 26%; PHQ-9, 20.5%; PCL-5, 9.6%; GHQ-12, 82.2%. Journalists assigned to cover the pandemic (n=54 (74%)) were significantly more anxious (p<0.05). Journalists who received counselling (n=38 (52%)) following the onset of the pandemic reported significantly fewer symptoms of anxiety (p<0.01), depression (p<0.01) and overall psychological distress (p<0.01). CONCLUSIONS: Journalists covering the COVID-19 pandemic are experiencing levels of anxiety and depression similar to those seen in first responders. Psychological therapy provided in a timely manner can significantly alleviate emotional distress.
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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 | 0.007 |
| 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 | 0.002 |
| 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".