Development, implementation and evaluation of a digital treatment for adolescents with chronic pain: a protocol for a multi-phase study
Bibliographic record
Abstract
Chronic pain in adolescents is an increasing public health concern with a significant physical, psychological, and social impact. This study aims to develop, implement, and evaluate DigiDOL-Ad, a digital psychosocial treatment for adolescents with chronic pain, supplemented by dedicated websites for their parents and teachers. This multicenter study will be conducted in four phases: (1) Development of the intervention framework and foundational planning; (2) Focus groups with adolescents with chronic pain, their parents, teachers, healthcare professionals and health authorities to identify specific needs and tailor the psychosocial treatment and related components; (3) Iterative usability testing of the digital treatment, using an a hermeneutical circle methodology to refine the design based on participant feedback; and (4) Evaluation of DigiDOL-Ad through a pre-treatment, post-treatment, and 3-month follow-up assessment. DigiDOL-Ad has the potential to improve the quality of life for adolescents with chronic pain. By leveraging digital health technologies, this innovative approach could establish a new benchmark for treating adolescents with chronic pain, emphasizing interdisciplinary and stakeholder-driven care. Clinical Trial Registration: clinicaltrials.gov, identifier NCT06765200.
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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 | 0.049 | 0.035 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 0.006 |
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".