<p>Effects and Experiences of Families Following a Web-Based Psychosocial Intervention for Children with Functional Abdominal Pain and Their Parents: A Mixed-Methods Pilot Randomized Controlled Trial</p>
Bibliographic record
Abstract
PURPOSE: To evaluate post-treatment efficacy of DARWeb (online psychosocial intervention for children with functional abdominal pain) using a randomized clinical trial design and combining quantitative and qualitative data. PATIENTS AND METHODS: Twenty-five families with children with FAP in the experimental group (EG: accessed to DARWeb) and 36 in the control group (CG: wait-list) were compared. Children and parents completed measures of abdominal pain severity (primary outcome), quality of life, and satisfaction. Moreover, children completed measures of depression, functional disability, catastrophizing and coping strategies; parents completed measures about parental responses to their children's pain. Families also answered open questions and were interviewed. RESULTS: A higher percentage of children in the EG achieved a significant clinical change in abdominal pain severity from the parents' perspective (28% in the EG vs 8.33% in the CG). There was a significantly greater reduction in pain frequency in the EG compared to the CG (both from the children's and parents' perspectives) from mixed repeated-measures analyses of variance (there was not a significant interaction in total scores of pain severity). A higher percentage of children in the EG improved in quality of life and depression compared to the CG (results from mixed methods repeated-measures analyses of variances were not significant). However, there were no differences for disability, pain catastrophizing or the coping strategies assessed from the children's perspective; neither from the parents' assessment of quality of life. There were significant interactions for parents' solicitousness responses and promotion of well behaviors in the expected directions. Families were quite satisfied with the intervention, and the qualitative results confirmed an improvement in pain and having learned important coping strategies. CONCLUSION: Our results support the efficacy of our intervention, but future studies are needed with different profiles of initial severity of the pain problem, longer follow-ups, and other conditions.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".