Children’s Behavioral Pain Cues: Implicit Automaticity and Control Dimensions in Observational Measures
Bibliographic record
Abstract
Some pain behaviors appear to be automatic, reflexive manifestations of pain, whereas others present as voluntarily controlled. This project examined whether this distinction would characterize pain cues used in observational pain measures for children aged 4-12. To develop a comprehensive list of cues, a systematic literature search of studies describing development of children's observational pain assessment tools was conducted using MEDLINE, PsycINFO, and Web of Science. Twenty-one articles satisfied the criteria. A total of 66 nonredundant pain behavior items were identified. To determine whether items would be perceived as automatic or controlled, 277 research participants rated each on multiple scales associated with the distinction. Factor analyses yielded three major factors: the "Automatic" factor included items related to facial expression, paralinguistics, and consolability; the "Controlled" factor included items related to intentional movements, verbalizations, and social actions; and the "Ambiguous" factor included items related to voluntary facial expressions. Pain behaviors in observational pain scales for children can be characterized as automatic, controlled, and ambiguous, supporting a dual-processing, neuroregulatory model of pain expression. These dimensions would be expected to influence judgments of the nature and severity of pain being experienced and the extent to which the child is attempting to control the social environment.
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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 | 0.015 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".