Establishing the psychometric properties and preferences for the Northern Pain Scale
Bibliographic record
Abstract
OBJECTIVES: A culturally relevant, evidence-based pain assessment scale in Inuktitut is needed. Psychometric properties and preferences for the Northern Pain Scale (NorthPS), a revised version of the Wong-Baker FACES scale, were examined. STUDY DESIGN: This repeated-measures, within-subjects study involved 2 face-to-face interviews held 2 weeks apart. METHODS: Participants were recruited from 2 schools and a community centre in Pangnirtung, Nunavut, Canada. Three pain scales (NorthPS, FACES and a numerical rating scale) were used to rate the pain portrayed in an adapted version of the Charleston Pediatric Pain Pictures (NorthCPPP, a series of 17 cartoon vignettes). RESULTS: The study involved 188 participants ranging in age from 5 to 83 years. Compared with the established FACES and numerical scales, the concurrent validity of the NorthPS was acceptable, with all 3 pain scales producing similar scores for the North CPPP vignettes. The youngest children were slightly more accurate during the second interview; otherwise, scoring accuracy was similar during both interviews. Accuracy was also similar across pain scales. Spearman correlations between NorthPS and other scales were lowest for the "No" pain vignettes, and for the youngest children. Internal consistency was acceptable for the NorthPS when compared with FACES and numerical scales. FACES was preferred by the majority of children and NorthPS was preferred by the majority of adults. CONCLUSIONS: NorthPS, a pain scale adapted to Inuit language and culture, was validated using the NorthCPPP with children and adults. The NorthPS is a well-understood, culturally and linguistically adapted option for the assessment of pain for Inuktitut speaking children and adults.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".