Influence of parental resilience on non-suicidal self-injury in adolescent cancer patients
Bibliographic record
Abstract
Objective: To examine the effect of a structured, DBT-based parental resilience training program on non-suicidal self-injury (NSSI) and related psychological symptoms in adolescents with cancer. Methods: This pre-post study without a control group enrolled 38 adolescent cancer patients (aged 13-18) with a history of NSSI, along with one of their parents. From March 2023 to May 2024, parents participated in a 4-week intervention consisting of three structured skills-training sessions (90 minutes each), weekly family group skills training, ongoing telephone counseling, and weekly consultations with a psychological support team. The Ottawa Self-Injury Scale was used to assess NSSI recurrence in adolescents. Anxiety and depression symptoms in adolescents were measured using the Generalized Anxiety Disorder Scale (GAD-7) and the Patient Health Questionnaire (PHQ-9). Parental resilience was evaluated with the Connor-Davidson Resilience Scale (CD-RISC). Results: All administered questionnaires were fully completed and valid, yielding a 100% response rate. Following the 4-week intervention, the recurrence rate of NSSI was 13.16% (5/38). Adolescents demonstrated significant reductions in both GAD-7 and PHQ-9 scores from pre- to post-treatment (P < 0.05), indicating marked improvements in anxiety and depression symptoms. Parental CD-RISC scores increased significantly (P < 0.05), reflecting enhanced resilience. Subgroup analyses revealed that these improvements were significant across both genders and age groups (<15 years vs. ≥15 years), with no statistically significant differences in the magnitude of change between subgroups (P > 0.05). Conclusions: A short-term, DBT-based parental resilience training program may improve caregiver resilience, reduce adolescent anxiety and depression, and lower short-term NSSI recurrence in adolescents with cancer. However, the lack of a control group and short follow-up limit the generalizability of findings. Further controlled studies with larger samples and longer follow-up are warranted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.002 |
| 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) | 0.001 | 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".