An examination of psychological distress and moral injury in journalists exposed to online harassment
Bibliographic record
Abstract
Background: Studies show that journalists face repeated, intense online harassment. While data reveal this is distressing to the profession, no detailed psychological study has been undertaken defining what this distress entails.Objective: To undertake a descriptive study examining the emotional wellbeing of journalists subject to online harassment.Method: Data from 246 journalists working for a Canadian news organization were collected via a secure study website. Information collected included demographics, harassment metrics (frequency and reasons for harassment); level of organizational support rated on a simple analog scale (0–10, with low scores indicating poor support); psychometric symptoms (anxiety: GAD-7; depression: PHQ-9; posttraumatic stress disorder: PCL-5; moral injury: Toronto Moral Injury Scale for Journalists).Results: The mean age of the sample was 43.07 (SD = 11.83) years. Fifty per cent were female. Harassment occurred at least weekly in 65 (26%) of the sample. Anxiety scores in the moderate to severe range were reported by 74 (30.1%) journalists while 34 (13.8%) had PTSD symptoms above the PCL-5 threshold for potential PTSD. Frequency of harassment correlated significantly with anxiety (r = 0.16, p = .014), depression (r = 0.15, p = .022), PTSD (r = 0.2, p = .002) and moral injury (r = 0.3, p < .002). Moral injury correlated significantly with anxiety (r = .40), depression (r = .41) and PTSD (r = .44; Spearman’s rho, p = .001 for all) scores. Organization support was rated as modest (M = 5.80, SD = 3.01).Conclusions: Frequency of online harassment is associated with a range of emotional responses. While anxiety is the predominant emotion, clinically significant symptoms of PTSD affect a substantial minority of journalists. Moral injury is linked to other indices of emotional distress. News organizations should do more to address the challenges posed by harassment and better support their journalists.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".