‘Fair and balanced?’: quality of suicide-related reporting on major US cable news networks
Bibliographic record
Abstract
BACKGROUND: The quality of news reports about suicide can influence suicide rates. Although many researchers have aimed to assess the general safety of news reporting in terms of adherence to responsible media guidelines, none have focused on major US cable networks, a key source of public information in North America and beyond. AIMS: To characterise and compare suicide-related reporting by major US cable television news networks across the ideological spectrum. METHOD: We searched a news archive (Factiva) for suicide-related transcripts from 'the big three' US cable television news networks (CNN, Fox News and MSNBC) over an 11-year inclusion interval (2012-2022). We included and coded segments with a major focus on suicide (death, attempt and/or thoughts) for general content, putatively harmful and protective characteristics and overarching narratives. We used chi-square tests to compare these variables across networks. RESULTS: We identified 612 unique suicide-related segments (CNN, 398; Fox News, 119; MSNBC, 95). Across all networks, these segments tended to focus on suicide death (72-89%) and presented stories about specific individuals (61-87%). Multiple putatively harmful characteristics were evident in segments across networks, including mention of a suicide method (42-52%) - with hanging (15-30%) and firearm use (12-20%) the most commonly mentioned - and stigmatising language (39-43%). Only 15 segments (2%) presented a story of survival. CONCLUSIONS: Coverage of suicide stories by major US cable news networks was often inconsistent with responsible reporting guidelines. Further engagement with networks and journalists is thus warranted.
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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 | 0.005 | 0.044 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".