Self-Injury in the News: A Content Analysis
Bibliographic record
Abstract
Non-suicidal self-injury (NSSI) has garnered increasing academic and media attention in society. While more awareness of NSSI is welcomed, inappropriate reporting of NSSI in media could heighten the potential for stigmatization and misunderstanding of NSSI and people who engage in it. Further, certain kinds of content (e.g., graphic imagery) may be harmful to people who self-injure (e.g., provoking urges to self-injure). These concerns notwithstanding, little research has focused on how NSSI has been portrayed in news media. Such knowledge would therefore represent a first step toward illuminating the nature of media depictions of NSSI and highlight potential areas to circumvent any concerns. Using content analysis, we explored how NSSI was portrayed in 568 online news articles about NSSI, published between 2007 and 2018, from top news sources in Australia, Canada, New Zealand, the United Kingdom, and the United States. Codes were developed based on prior research investigating online NSSI content, and the available existing and proposed media guidelines for the reporting of NSSI at the time of the study. While the overall tone of the examined articles was often neutral, areas of concern included: most articles detailing specific NSSI methods, the frequent inclusion of negative imagery, an absence of clear communication about what NSSI is and why people self-injure, the use of sensationalist and stigmatizing language, and a lack of helpful resources. These preliminary findings suggest the utility of a set of newly developed media guidelines on the reporting of NSSI as one component in an effort to address the stigmatization and misunderstanding of NSSI and individuals who self-injure.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.020 | 0.023 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".