Differential item functioning related to ethnicity in an oral health‐related quality of life measure
Bibliographic record
Abstract
OBJECTIVE: To assess whether an oral health-related quality of life (OHRQoL)measure showed differential item functioning (DIF) by ethnicity. METHODS: A simple random sample of 12- and 13-year-old schoolchildren enrolled in the Taranaki District Health Board's school dental service, New Zealand. Each child (n = 430) completed the Child Perception Questionnaire (CPQ(11-14)) in the dental clinic waiting room, prior to a dental examination. The dataset included age, gender, ethnicity, and deprivation status. The general principle of the analytic plan was that equal scores from each CPQ(11-14) item were expected from both non-Mäori and Mäori groups regardless of their ethnic group. Ordinal logistic regression was performed. The dependent variables were the CPQ(11-14) items. The ethnicity group and each CPQ(11-14) domain score were the independent variables. Non-uniform DIF was assessed through adding an interaction term for each CPQ(11-14) sub-scale. RESULTS: Non-uniform DIF was found in two items, one in the Functional Limitations sub-scale and another in the Social Well-being sub-scale. Uniform DIF was found in one item of the Emotional Well-being sub-scale. CONCLUSION: Both non-uniform and uniform DIF by ethnicity was found in three of 37 items of the CPQ(11-14) questionnaire, showing it is important to perform DIF analysis when applying OHRQoL measures.
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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 | 0.005 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".