The role of developmental factors in predicting young children's use of a self-report scale for pain
Bibliographic record
Abstract
Accurate pain assessment is the foundation for effective pain management in children. At present, there is no clear consensus regarding the age at which young children are able to appropriately use self-report scales for pain. This study examined young children's ability to use the Faces Pain Scale-Revised; (FPS-R; [Hicks CL, von Baeyer CL, Spafford PA, van Korlaar I, Goodenough B. The Faces Pain Scale-Revised: toward a common metric in pediatric pain measurement. Pain 2001; 93: 173-83]) for pain in response to vignettes and investigated the role of developmental factors in predicting their ability to use the scale. One hundred and twelve healthy children (3-6 years old) were assessed for their ability to accurately use a common faces scale to rate pain in hypothetical vignettes depicting pain scenarios common in childhood. Accuracy was determined by considering whether children's judgements of pain severity matched the pain severity depicted in the various vignettes. Children were also administered measures of numerical reasoning, language, and overall cognitive development. Results indicated that 5- and 6-year-old children were significantly more accurate in their use of the FPS-R in response to the vignettes than 4-year-old children, who in turn were significantly more accurate than 3-year-old children. However, over half of the 6-year-olds demonstrated difficulties using the FPS-R in response to the vignettes. Child age was the only significant predictor of children's ability to use the scale in response to the vignettes. Thus, a substantial number of young children experienced difficulties using the FPS-R when rating pain in hypothetical vignettes, although the ability to use the scale did improve with age.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".