Longitudinal Narrative Analysis of Parent Experiences During Graded Exposure Treatment for Children With Chronic Pain
Bibliographic record
Abstract
OBJECTIVES: Parents have a vital influence over their child's chronic pain treatment and management. Graded exposure in vivo treatment (GET) is emerging as a promising intervention for youth with chronic pain. Yet, little is known about how parents perceive GET and its impact on their child's pain condition. This study aimed to characterize caregivers' experiences over the course of their child's GET using longitudinal coding and thematic analysis of parent narratives. MATERIALS AND METHODS: Parent narratives of 15 youth who participated in GET for pediatric chronic pain (GET Living) were elicited from an unstructured dialogue at the start of each treatment session held between the parent(s) and pain psychologist. Narratives were coded for affect and content, and trends were examined in these codes across sessions. Common themes in parent narratives were developed through inductive thematic analysis. RESULTS: Parents showed an increase in positive affect, treatment confidence, and optimism over the course of treatment. Narratives also expressed more benefit-finding/growth and less anxiety and protectiveness across GET sessions, with more parents having a resolved orientation towards their child's pain by the final session. Five common themes were generated: Self-Awareness, Understanding of Their Child's Perspective, Perceived Treatment Benefit, Internalization of Treatment Principles, and Hopeful Concern for the Future. DISCUSSION: Analysis of parent narratives provides a rich and unique method for understanding a parent's journey during their child's chronic pain treatment. Clinical application of our findings can be used to guide future developments of targeted topics and interventions in the context of parenting a child with chronic pain.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| 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".