“Ow!”: Spontaneous Verbal Pain Expression Among Young Children During Immunization
Bibliographic record
Abstract
OBJECTIVES: Although self-reports are a commonly used means of assessing pain in clinical settings, little is understood about the nature of children's spontaneous verbal expressions of pain. The purpose of this study was to describe verbalizations of pain among children receiving a preschool immunization and to examine how pain verbalizations correspond to children's facial expressions and self-reports of pain intensity. METHODS: Fifty-eight children between the ages of 4 years 8 months and 6 years 3 months (67% female) were videotaped while receiving their routine preschool immunization. Global ratings of facial expression and detailed transcription and coding of pain verbalizations were undertaken. Children provided self-reports of pain using a 7-point faces pain scale. RESULTS: Fifty-three percent of children used verbalizations spontaneously to express their pain. The modal verbalization was the interjection "Ow!," which expressed negative affect and was specific to the experience of pain. Older children were less likely to use verbalizations to express their pain. Children who used verbalizations to express pain displayed greater facial reactions to pain and rated their pain experience as being more intense than children who did not use words to express their pain. DISCUSSION: Results indicate that many young children do not spontaneously use verbalizations to express pain from immunization. When 5-year-olds use verbalizations to express pain, the verbalizations are most often brief statements that express negative affect and directly pertain to pain. Knowledge of how children verbalize pain may lead to an improved ability to assess and manage pediatric pain.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".