Child attention to pain and pain tolerance are dependent upon anxiety and attention control: An eye‐tracking study
Bibliographic record
Abstract
BACKGROUND: Pain is common and can be debilitating in childhood. Theoretical models propose that attention to pain plays a key role in pain outcomes, however, very little research has investigated this in youth. This study examined how anxiety-related variables and attention control interacted to predict children's attention to pain cues using eye-tracking methodology, and their pain tolerance on the cold pressor test (CPT). METHODS: Children aged 8-17 years had their eye-gaze tracked whilst they viewed photographs of other children displaying painful facial expressions during the CPT, before completing the CPT themselves. Children also completed self-report measures of anxiety and attention control. RESULTS: Findings indicated that anxiety and attention control did not impact children's initial fixations on pain or neutral faces, but did impact how long they dwelled on pain versus neutral faces. For children reporting low levels of attention control, higher anxiety was associated with less dwell time on pain faces as opposed to neutral faces, and the opposite pattern was observed for children with high attention control. Anxiety and attention control also interacted to predict pain outcomes. For children with low attention control, increasing anxiety was associated with anticipating more pain and tolerating pain for less time. CONCLUSIONS: This is the first study to examine children's attention to pain cues using eye-tracking technology in the context of a salient painful experience. Data suggest that attention control is an important moderator of anxiety on multiple outcomes relevant to young people's pain experiences. SIGNIFICANCE: This study uses eye tracking to study attention to pain cues in children. Attention control is an important moderator of anxiety on attention bias to pain and tolerance of cold pressor pain in youth.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".