Jelena Dokic's suicide-related social media post and the worldwide media's portrayal of a story of survival: a natural experiment
Bibliographic record
Abstract
Objective: Coverage and public communication about suicide represent a major public health concern given the potential for identification and imitation. Yet when celebrities survive a suicidal crisis, it presents an opportunity to model adaptive coping. Tennis star Jelena Dokic's June 2022 Instagram post recounting her experience overcoming suicidal thoughts represents a unique natural experiment to characterize media coverage of a celebrity survival event. Methods: We searched Google News and the entire University of Toronto library catalogue for articles about Dokic's post. We divided articles according to world region of publication: (a) Australia & New Zealand, (b) United States & Canada, and (c) United Kingdom & Ireland. We coded articles for content and used Chi-squared analyses to identify differences including adherence to responsible media reporting guidelines. Results: We identified 73 articles of which 71 were available for coding. Almost all articles positioned Dokic's story as one of survival and conveyed alternatives to suicide (94%). However, 56 (79%) highlighted a suicide method that Dokic mentioned in her post and 18 (25%) inaccurately described Dokic as disclosing that she had attempted suicide when her post only conveyed suicidal thoughts. In general, adherence to responsible reporting guidelines appeared stronger in articles published in Australia & New Zealand. Conclusions: We found that the international media extensively covered Dokic's story of survival including substantial helpful information but also some misinformation and content that violates responsible reporting guidelines. Greater adherence by media in Australia & New Zealand may be due to more robust implementation of responsible media guidelines in the region.
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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 | 0.006 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".