Caregiver accuracy in detecting deception in facial expressions of pain in children
Bibliographic record
Abstract
Facial expressions provide a primary source of inference about a child's pain. Although facial expressions typically appear spontaneous, children have some capacity to fake or suppress displays of pain, thereby potentially misleading caregiver judgments. The present study was designed to compare accuracy of different groups of caregivers in detecting deception in children's facial expressions of pain when voluntarily controlled. Caregivers (15 pediatricians, 15 pediatric nurses, and 15 parents) viewed 48 video clips of children, 12 in each of 4 conditions (genuine pain, faked pain, suppressed pain, neutral baseline), and judged which condition was apparent to them. A 3 (group: pediatrician vs pediatric nurse vs parent)×4 (condition: genuine vs faked vs suppressed vs neutral) mixed analysis of variance (ANOVA) of judgment accuracies revealed a significant main effect of group, with nurses demonstrating higher overall accuracy scores than parents, and pediatricians not differing from either group. As well, all caregivers, regardless of group, demonstrated the lowest accuracy when viewing the genuine condition, relative to the faked and suppressed conditions, with accuracy for the neutral condition not differing significantly from the other conditions. Overall, caregivers were more successful at identifying faked and suppressed than genuine expressions of pain in children, and pediatric nurses fared better overall in judgment accuracy than parents.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.005 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".