Journalists covering the refugee and migration crisis are affected by moral injury not PTSD
Bibliographic record
Abstract
Objective To explore the emotional health of journalists covering the migrations of refugees across Europe. Design Descriptive. A secure website was established and participants were given their unique identifying number and password to access the site. Setting Newsrooms and in the field. Participants Responses were received from 80 (70.2%) of 114 journalists from nine news organisations. Main outcome measures Symptoms of PTSD (Impact of Events Scale-revised), depression (Beck Depression Inventory-Revised) and moral injury (Moral Injury Events Scale-revised). Results Symptoms of PTSD were not prominent, but those pertaining to moral injury and guilt were. Moral injury was associated with being a parent ( p = .031), working alone ( p = .02), a recent increase in workload ( p = .017), a belief that organisational support is lacking ( p = .046) and poor control over resources needed to report the story ( p = .027). A significant association was found between guilt and moral injury ( p = .01) with guilt more likely to occur in journalists who reported covering the migrant story close to home ( p = .011) and who divulged stepping outside their role as a journalist to assist migrants ( p = .014). Effect sizes ( d) ranged from .47 to .71. Conclusions On one level, the relatively low scores on conventional psychometric measures of PTSD and depression are reassuring. However, our data confirm that moral injury is a different construct from DSM-defined trauma response syndromes, one that potentially comes with its own set of long-term maladaptive behaviours and adjustment problems.
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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 | 0.014 |
| 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) | 0.005 | 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".