Response biases in preschool children's ratings of pain in hypothetical situations
Bibliographic record
Abstract
Response biases are systematic biases in responding to test items that are unrelated to the content of the items. Examples often reported in young children include choosing only the lowest or highest anchors of a scale, or choosing a left-to-right sequence of responses. We investigated the presence of response biases in young children's ratings of pain in hypothetical situations, as a way of gauging their developing understanding of a pain scale over the preschool years. Children aged 3-5 years (N=185) rated items from the Charleston Pediatric Pain Pictures (CPPP) using the Faces Pain Scale-Revised (FPS-R). Response biases were identified objectively by computer pattern identification. Anchor biases (choosing the lowest and highest pain faces) occurred in 16% of children. Left-right or right-left sequences occurred in 35%. Monte Carlo simulation established that such patterns occur infrequently by chance (<3% for anchor biases; <6% for sequence biases). Response biases were identified more often in younger than older children. These results reveal that response biases are common in children under 5 years. Clinicians should consider self-report pain ratings from preschoolers with caution, seek complementary observational assessment, and investigate discrepancies between self-report and observational estimates of pain. Simplified forms, instructions, and methods of administration for self-report scales should be developed and validated for use with 3- and 4-year-olds.
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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 | 0.026 | 0.031 |
| 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".