The role of executive functioning in children's attentional pain control: An experimental analysis
Bibliographic record
Abstract
Directing attention away from pain is often used in children's pain treatment programs to control pain. However, empirical evidence concerning its effectiveness is inconclusive. We therefore sought to understand other influencing factors, including executive function and its role in the pain experience. This study investigates the role of executive functioning in the effectiveness of distraction. School children (n=164) completed executive functioning tasks (inhibition, switching, and working memory) and performed a cold-pressor task. One half of the children simultaneously performed a distracting tone-detection task; the other half did not. Results showed that participants in the distraction group were engaged in the distraction task and were reported to pay significantly less attention to pain than controls. Executive functioning influenced distraction task engagement. More specifically, participants with good inhibition and working memory abilities performed the distraction task better; participants with good switching abilities reported having paid more attention to the distraction task. Furthermore, distraction was found to be ineffective in reducing pain intensity and affect. Executive functioning did not influence the effectiveness of distraction. However, a relationship was found between executive functioning and pain affect, indicating that participants with good inhibition and working memory abilities experienced the cold-pressor task as less stressful and unpleasant. Our findings suggest that distraction as a process for managing pain is complex. While it appears that executive function may play a role in adult distraction, in this study it did not direct attention away from pain. It may instead be involved in the overall pain experience.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".