Bibliographic record
Abstract
A growing body of research points to the salience of the Internet and mobile material among individuals who self-injure. However, to date, no research has investigated the mobile apps related to nonsuicidal self-injury (NSSI). Such information would clarify which apps may be useful for those who self-injure while highlighting whether app-related content warrants improvement. The current study examined the content and usability of NSSI apps available on the two largest app-related platforms (Google Play and iTunes). Using content analysis, apps were examined regarding their content (e.g., presence of NSSI myths and types of coping strategies) as well as usability (e.g., app performance). Results indicate that NSSI apps have varied content, with few developed by, or affiliated with, a trusted source (e.g., university). NSSI apps tend to not propagate NSSI myths that vary with respect to the quality of coping strategies offered. They also tend to be rated favorably in terms of their usability. Overall, the present findings add to the NSSI literature and highlight several implications and avenues for future work, which are discussed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".