Geographical differences in the stress and distress of climate change journalists: An observational study
Bibliographic record
Abstract
Objectives The aim of this study is to investigate potential inter-continental mental health differences in journalists covering climate-related events. Design Descriptive, cross sectional. Setting Internet-based study. Participants Journalists recruited from the Oxford Climate Journalist Network: 268 of 561 (48.6%) journalists from 89 countries completed the study. Main Outcome Measures Questions related to physical threat and loss secondary to climate change. Symptoms of anxiety (Generalized Anxiety Disorder-7-item scale [GAD-7]), depression (Patient Health Questionnaire-9 [PHQ-9]), posttraumatic stress disorder (PCL-5) and Moral Injury (Toronto Moral Injury Scale for Journalists [TMIS-J]). Results More African and Asian journalists felt physically threatened than journalists in Europe ( p < .001 and p = .002, respectively). More journalists in Africa had lost a family member to climate change than journalists in the Americas ( p = .009), and Asia and Europe ( p < .001 for both). More journalists in Africa, Asia, and the Americas had lost a friend to climate change compared to journalists in Europe ( p < .001, p = .003, and p = .001, respectively). There were higher PTSD-intrusion scores in African and Asian than European journalists ( p = .001 and p < .001, respectively) and higher PTSD-avoidance scores in African and Asian than European journalists ( p = .014 and p = .001, respectively. African and Asian journalists were less likely to receive psychotherapy than European journalists ( p < .001 for both). Conclusions Given the enduring challenges posed by climate change, addressing these inequalities in journalists’ care should not be delayed any further.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".