Emotion regulatory function of parent attention to child pain and associated implications for parental pain control behaviour
Bibliographic record
Abstract
We investigated the function of parental attention to child pain in regulating parental distress and pain control behaviour when observing their child performing a painful (cold pressor) task (CPT); we also studied the moderating role of parental state anxiety. Participants were 62 schoolchildren and one of their parents. Parental attention towards or away from child pain (ie, attend to pain vs avoid pain) was experimentally manipulated during a viewing task pairing unfamiliar children's neutral and pain faces. Before and after the viewing task, parental distress regulation was assessed by heart rate (HR) and heart rate variability (HRV). In a subsequent phase, parents observed their own child perform a CPT task, allowing assessment of parental pain control behaviour (indexed by latency to stop their child's CPT performance) and parental distress, which was assessed via self-report before and after observation of child CPT performance. Eye tracking during the viewing task and self-reported attention to own child's pain confirmed successful attention manipulation. Further, findings indicated that the effect of attentional strategy on parental emotion regulation (indexed by HR, self-report) and pain control behaviour depended on parents' state anxiety. Specifically, whereas low anxious parents reported more distress and demonstrated more pain control behaviour in the Attend to Pain condition, high anxious parents reported more distress and showed more pain control behaviour in the Avoid Pain condition. This inverse pattern was likewise apparent in physiological distress indices (HR) in response to the initial viewing task. Theoretical/clinical implications and further research directions 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 | 0.002 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".