Assessing Adherence to Responsible Reporting of Suicide Guidelines in the Canadian News Media: A 1-year Examination of Day-to-day Suicide Coverage: Évaluer la conformité au journalisme responsable en matière de directives sur le suicide dans les médias canadiens d’information: Un examen d’une année de la couverture quotidienne du suicide
Bibliographic record
Abstract
OBJECTIVE: This study aims to examine routine day-to-day suicide reporting in the Canadian media, giving a descriptive overview of the tone and content of news articles. The primary objective is to assess adherence to responsible reporting of suicide recommendations in news articles about suicide. A secondary objective is to categorize these articles according to their focus. A tertiary objective is to compare guideline adherence across the different categories of articles. METHODS: We collected news articles containing the keyword "suicide" from 47 Canadian news sources between April 1, 2019, and March 31, 2020. Articles were read and coded for their adherence to responsible reporting of suicide recommendations. Articles were also allotted into categories according to their focus and primary suicide discussed. Frequency counts and percentages of adherence were calculated for all key variables-both overall and by category of article. Chi-square tests were also conducted to assess for variations in adherence by category of article. RESULTS: The procedures resulted in 1,330 coded articles. On the one hand, there was high overall adherence to several recommendations. For example, over 80% of articles did not give a monocausal explanation, glamourize the death, appear on the front page, include sensational language, or use discouraged words. On the other hand, there was low adherence to other recommendations, especially those related to putatively protective content. For example, less than 25% included help-seeking information, quoted an expert, or included educational content. Cross-category analysis indicated that articles about events/policies/research and Indigenous people had the highest proportions of adherence, while articles about murder-suicide and high-profile suicides had the lowest adherence. CONCLUSIONS: While a substantial proportion of articles generally adhere to suicide reporting recommendations, several guidelines are frequently underapplied, especially those concerning putatively helpful content. This indicates room for improvement in the responsible reporting of suicide.
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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 | 0.013 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.011 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".