Associations of suicide-related media reporting characteristics with help-seeking and suicide in Oregon and Washington
Bibliographic record
Abstract
OBJECTIVE: Specific content characteristics of suicide media reporting might differentially impact suicides in the population, but studies have not considered the overarching theme of the respective media stories and other relevant outcomes besides suicide, such as help-seeking behaviours. METHODS: We obtained 5652 media reports related to suicide from 6 print, 44 broadcast and 251 online sources in Oregon and Washington states, published between April 2019 and March 2020. We conducted a content analysis of stories regarding their overarching focus and specific content characteristics based on media recommendations for suicide reporting. We applied logistic regression analyses to assess how focus and content characteristics were associated with subsequent calls to the US National Suicide Prevention Lifeline (Lifeline) and suicides in these two states in the week after publication compared to a control time period. RESULTS: Compared to a focus on suicide death, a focus on suicidal ideation, suicide prevention, healing stories, community suicide crises/suicide clusters and homicide suicide was associated with more calls. As compared to a focus on suicide death, stories on suicide prevention and stories on community suicide crises/suicide clusters were also associated with no increase in suicides. Regarding specific content characteristics, there were associations that were largely consistent with previous work in the area, for example, an association of celebrity suicide reporting with increases in suicide. CONCLUSION: The overall focus of a media story may influence help-seeking and suicides, and several story characteristics appear to be related to both outcomes. More research is needed to investigate possible causal effects and pathways.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".