Developing a Text Messaging Intervention to Reduce Deliberate Self-Harm in Chinese Adolescents: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Deliberate self-harm is common during adolescence and can have detrimental consequences for the well-being of adolescents. Although it is sometimes difficult to engage adolescents in traditional psychotherapies for deliberate self-harm, SMS text messaging has been shown to be promising for cost-effective and low-intensity interventions. OBJECTIVE: This study aimed to investigate the views of Chinese adolescents with deliberate self-harm about SMS text messaging interventions in order to develop an acceptable and culturally competent intervention for adolescents with deliberate self-harm. METHODS: Semistructured interviews were conducted with 23 adolescents who had experience with deliberate self-harm. The transcripts of the interviews were analyzed using thematic analysis. RESULTS: Four themes were identified: beneficial perception of receiving messages, short frequency and duration of messages, caring content in messages, and specific times for sending messages. Most of the participants perceived SMS text messaging interventions to be beneficial. The key factors that emerged for the content of the intervention included encouragement and company, feeling like a virtual friend, providing coping strategies, and individualized messages. In addition, the preferred frequency and duration of the SMS text messaging intervention were identified. CONCLUSIONS: Our study will help in the development of a culturally appropriate SMS text messaging intervention for adolescents with deliberate self-harm. It has the potential to decrease deliberate self-harm instances by providing acceptable support for adolescents with deliberate self-harm who may be reluctant to seek face-to-face psychotherapies.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".