Optimizing Numeric Pain Rating Scale administration for children: The effects of verbal anchor phrases
Bibliographic record
Abstract
Background: The 0–10 Verbal Numeric Rating Scale (VNRS) is commonly used to obtain self-reports of pain intensity in school-age children, but there is no standard verbal descriptor to define the most severe pain.Aims: The aim of this study was to determine how verbal anchor phrases defining 10/10 on the VNRS are associated with children’s reports of pain.Methods and Results: Study 1. Children (N = 131, age 6–11) rated hypothetical pain vignettes using six anchor phrases; scores were compared with criterion ratings. Though expected effects of age and vignette were found, no effects were found for variations in anchors. Study 2. Pediatric nurses (N = 102) were asked how they would instruct a child to use the VNRS. Common themes of “the worst hurt you could ever imagine” and “the worst hurt you have ever had” to define 10/10 were identified. Study 3. Children’s hospital patients (N = 27, age 8–14) rated pain from a routine injection using four versions of the VNRS. Differences in ratings ranging from one to seven points on the scale occurred in the scores of 70% of children when the top anchor phrase was changed. Common themes in children’s descriptions of 10/10 pain intensity were “hurts really bad” and “hurts very much.”Discussion: This research supports attention to the details of instructions that health care professionals use when administering the VNRS. Use of the anchor phrase “the worst hurt you could ever imagine” is recommended for English-speaking, school-age children. Details of administration of the VNRS should be standardized and documented in research reports and in clinical use.
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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 | 0.039 | 0.164 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".