A Time-Series Analysis of News Media Coverage of Suicide in Canada from 2019 to 2023: Une analyse de séries chronologiques de la couverture responsable du suicide par les médias au Canada de 2019 à 2023
Bibliographic record
Abstract
OBJECTIVE: Evidence suggests that the media can play a role in preventing suicide, as well as contributing to suicide contagion. As such, the primary objective is to assess adherence to responsible reporting of suicide recommendations in news articles about suicide over time. A secondary objective is to assess whether reporting changed significantly during the COVID-19 pandemic. The tertiary objective is to assess overall patterns regarding types of suicide reported. METHODS: We collected news articles with the keyword "suicide" from 47 Canadian news sources between April 1, 2019, and March 31, 2023. Articles were coded for adherence to key responsible reporting of suicide guidelines. Frequency counts and percentages of adherence were calculated for all key variables. Time series analyses using a Generalized Linear Autoregressive Moving Average model assessed for adherence trends over time, including measuring for any changes during the COVID-19 years. RESULTS: Study procedures resulted in 3,232 coded news articles. Overall, the results indicate that adherence to the guidelines has moderately improved over the course of the 4-year period. This is especially true for recommendations regarding avoiding putatively harmful content, such as detailed descriptions of the suicide method. Similar improvements were seen in adherence to guidelines related to the inclusion of putatively helpful content, with significantly more articles providing help-seeking information. However, in the final year of the study, less than a third of articles included educational content about suicide, help-seeking information, or quotes from suicide experts. Reporting of suicide during the COVID-19 period showed some positive improvements; however, these were not sustained after the pandemic ended. CONCLUSIONS: On the plus side, adherence to responsible suicide reporting guidelines improved over the 4-year period, especially for recommendations concerning putatively helpful content. However, there remains room for improvement regarding the inclusion of putatively protective content such as including help-seeking information, educating about suicide, and quoting experts.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".