Caregivers' attentional bias to pain
Bibliographic record
Abstract
Attentional bias to pain among family caregivers of patients with pain may enhance the detection of pain behaviors in patients. However, both relatively high and low levels of attentional bias may increase disagreement between patients and caregivers in reporting pain behaviors. This study aims to provide further evidence for the presence of attentional bias to pain among family caregivers, to examine the association between caregivers' attentional bias to pain and detecting pain behaviors, and test whether caregivers' attentional bias to pain is curvilinearly related to patient and caregiver disagreement in reporting pain behaviors. The sample consisted of 96 caregivers, 94 patients with chronic pain, and 42 control participants. Caregivers and controls completed a dot-probe task assessing attention to painful and happy stimuli. Both patients and caregivers completed a checklist assessing patients' pain behavior. Although caregivers did not respond faster to pain congruent than pain incongruent trials, caregiver responses were slower in pain incongruent trials compared with happy incongruent trials. Caregivers showed more bias toward pain faces than happy faces, whereas control participants showed more bias toward happy faces than pain faces. Importantly, caregivers' attentional bias to pain was significantly positively associated with reporting pain behaviors in patients above and beyond pain severity. It is reassuring that attentional bias to pain was not related to disagreement between patients and caregivers in reporting pain behaviors. In other words, attentional bias does not seem to cause overestimation of pain signals.
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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 | 0.002 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".