Parental narrative style moderates the relation between pain-related attention and memory biases in youth with chronic pain
Bibliographic record
Abstract
ABSTRACT: Negatively biased pain memories robustly predict maladaptive pain outcomes in children. Both attention bias to pain and parental narrative style have been linked with the development of these negative biases, with previous studies indicating that how parents talk to their child about the pain might buffer the influence of children's attention bias to pain on the development of such negatively biased pain memories. This study investigated the moderating role of parental narrative style in the relation between pain-related attention and memory biases in a pediatric chronic pain sample who underwent a cold pressor task. Participants were 85 youth-parent dyads who reminisced about youth's painful event. Eye-tracking technology was used to assess youth's attention bias to pain information, whereas youth's pain-related memories were elicited 1 month later through telephone interview. Results indicated that a parental narrative style using less repetitive yes-no questions, more emotion words, and less fear words buffered the influence of high levels of youth's attention bias to pain in the development of negatively biased pain memories. Opposite effects were observed for youth with low levels of attention bias to pain. Current findings corroborate earlier results on parental reminiscing in the context of pain (memories) but stress the importance of matching narrative style with child characteristics, such as child attention bias to pain, in the development of negatively biased pain memories. Future avenues for parent-child reminiscing and clinical implications for pediatric chronic pain are discussed.
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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".