Interpersonal Dyadic Influences of Pain Catastrophizing Between Caregivers and Children With Chronic Pain
Bibliographic record
Abstract
OBJECTIVES: Pain catastrophizing is an important predictor of pain-related outcomes. Caregiver and child levels of catastrophizing about child chronic pain are associated cross-sectionally, yet predictive associations testing interpersonal influences within caregiver-child dyads are lacking. The present study tested caregiver and child influences on partner catastrophizing about child pain over a period of 1 month following initiation of interdisciplinary pain treatment and examined whether the change in pain catastrophizing was associated with child pain interference. MATERIALS AND METHODS: A total of 113 caregiver-child dyads (Mage=14.41) completed measures at the time of initiating care at a pediatric tertiary outpatient pain management clinic (baseline) and ∼1 month later. Caregivers and children independently reported on catastrophizing about child pain and child pain interference at baseline and 1-month follow-up. RESULTS: Caregiver and child pain catastrophizing decreased over 1 month following initial interdisciplinary pain evaluation, with average scores remaining in the moderate to high range. Change in caregiver, but not child, catastrophizing about child pain was predicted by partner baseline pain catastrophizing. Decreases in catastrophizing about child pain were associated with within-person improvement in ratings of child pain interference. DISCUSSION: In the short period following initial pain evaluation, caregivers and children evidenced reductions in pain catastrophizing, which were associated with increased child function. Findings highlight the important role of child cognitive-affective responses to pain in influencing caregiver catastrophizing about child pain. Understanding the individual contributions children and caregivers make to interpersonal pain processes will inform future family-level clinical interventions.
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 | 0.001 | 0.009 |
| 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 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".