Experiences of an Online Treatment for Adolescents With Nonsuicidal Self-injury and Their Caregivers: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Nonsuicidal self-injury (NSSI) is common in adolescence and is associated with several adverse outcomes. Despite this, few established treatment options exist. Online treatment seems promising for several conditions; however, knowledge on NSSI is scarce. It is important to explore how online treatment for NSSI is experienced to improve such interventions and learn more about factors that are important in the treatment of adolescents with NSSI. OBJECTIVE: This study aims to explore the experiences of a novel online treatment for adolescents with NSSI and their caregivers. METHODS: A qualitative study using thematic analysis was conducted through semistructured interviews with 9 adolescents and 11 caregivers at treatment termination or at the 6-month follow-up of the online emotion regulation individual therapy for adolescents. RESULTS: A total of 3 overarching themes were identified. The theme support can come in different shapes showed how support could be attained through both interaction with the therapist as well as through the format itself (such as through the fictional characters in the material and the mobile app). Caregivers found it helpful to have their own online course, and adolescents accepted their involvement. The theme self-responsibility can be empowering as well as distressing showed that self-responsibility was highly appreciated (such as deciding when and how to engage in treatment) but also challenging; it caused occasional distress for some. The theme acquiring new skills and treatment effects showed the advantages and challenges of learning several different emotion regulation skills and that decreased emotion regulation difficulties were important treatment outcomes for adolescents. In addition, several different skills seemed to facilitate emotion regulation, and having access to such skills could hinder NSSI. CONCLUSIONS: Online emotion regulation individual therapy for adolescents seems to offer an accepted way to deliver family interventions for this target group; facilitate skills training with several means of support, including support from both the mobile app and the therapist; contribute to decreasing emotion regulation difficulties and teaching skills that could hinder NSSI; and cause (in some individuals) distress because of the self-responsibility that is inherent to online formats, which needs to be addressed.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".