Media coverage of Canadian Veterans, with a focus on post traumatic stress disorder and suicide
Bibliographic record
Abstract
BACKGROUND: A large corpus of research indicates that the media plays a key role in shaping public beliefs, opinions and attitudes towards social groups. Some research from the United States indicates that military Veterans are sometimes framed in a stereotypical and stigmatizing manner, however there is a lack of research on Canadian media coverage of Veterans. As such, the overarching aim of this study is to assess the tone and content of Canadian media coverage of military Veterans, with a focus on PTSD and suicide. The first objective is to document and analyze common themes, content and temporal patterns in Canadian media coverage of Veterans per se. The second objective is to examine common themes and content in the sub-set of articles having PTSD as a theme. The third objective is to assess adherence to responsible reporting of suicide guidelines in the sub-set of articles having suicide as a theme. METHODS: We used validated and systematic methods including use of key words, retrieval software and inter-rater reliability tests to collect and code news articles (N = 915) about Veterans from over 50 media sources during a 12-month period, with specific coding of articles about PTSD (N = 93) and suicide (N = 61). RESULTS: Analysis revealed that the most common theme is 'honour or commemoration of Veterans' which occurred in over half of the articles. In contrast 14% of articles focused on danger, violence or criminality. In the sub-set of articles with PTSD as a theme, over 60% focused on danger, violence or criminality, while only around 1 in 3 focused on recovery, rehabilitation, or health/social service intervention. In the sub-set of articles about suicide, there was generally strong adherence to responsible reporting guidelines, though less than 5% gave help-seeking information. Moreover, most reporting on PTSD and suicide focused on a single anomalous murder-suicide incident, with few articles about suicide prevention, helpful resources and modifiable risk factors. CONCLUSIONS: The results reveal some encouraging findings as well as a need to diversify media coverage of Canadian Veterans. This could be achieved through targeted educational outreach to help Canadian journalists responsibly report on Veterans and their mental health issues.
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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 | 0.002 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.031 | 0.034 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".