Characterizing the Pain Narratives of Parents of Youth With Chronic Pain
Bibliographic record
Abstract
OBJECTIVES: Questionnaire-based research has shown that parents exert a powerful influence on and are profoundly influenced by living with a child with chronic pain. Examination of parents' pain narratives through an observational lens offers an alternative approach to understanding the complexity of pediatric chronic pain; however, the narratives of parents of youth with chronic pain have been largely overlooked. The present study aimed to characterize the vulnerability-based and resilience-based aspects of the pain narratives of parents of youth with chronic pain. METHODS: Pain narratives of 46 parents were recorded during the baseline session as part of 2 clinical trials evaluating a behavioral intervention for parents of youth with chronic pain. The narratives were coded for aspects of pain-related vulnerability and resilience. RESULTS: Using exploratory cluster analysis, 2 styles of parents' pain narratives were identified. Distress narratives were characterized by more negative affect and an exclusively unresolved orientation toward the child's diagnosis of chronic pain, whereas resilience narratives were characterized by positive affect and a predominantly resolved orientation toward the child's diagnosis. Preliminary support for the validity of these clusters was provided through our finding of differences between clusters in parental pain catastrophizing about child pain (helplessness). DISCUSSION: Findings highlight the multidimensional nature of parents' experience of their child's pain problem. Clinical implications in terms of assessment and treatment are discussed.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".