BODY-Q Normative Scores: Psychometric Validation of the BODY-Q in the General Population in Europe and North America
Bibliographic record
Abstract
Background: BODY-Q is a rigorously developed patient-reported outcome measure designed to measure outcomes of weight loss and body contouring patients. To allow interpretation and comparison of BODY-Q scores across studies, normative BODY-Q values were generated from the general population. The aim of this study was to examine the psychometric properties of BODY-Q in the normative population. Methods: Data were collected using two crowdsourcing platforms (Prolific and Amazon Mechanical Turk) in 12 European and North American countries. Rasch measurement theory (RMT) was used to examine reliability and validity of BODY-Q scales. Results: RMT analysis supported the psychometric properties of BODY-Q in the normative sample with ordered thresholds in all items and nonsignificant chi-square values for 167 of 176 items. Reliability was high with person separation index of greater than or equal to 0.70 in 20 of 22 scales and Cronbach alpha values of greater than or equal to 0.90 in 17 of 22 scales. Mean scale scores measuring appearance, health-related quality of life, and eating-related concerns scales varied as predicted across subgroups with higher scores reported by participants who were more satisfied with their weight. Analysis to explore differential item functioning by sample (normative versus field-test) flagged some potential issues, but subsequent comparison of adjusted and unadjusted person estimates provided evidence that the scoring algorithm worked equivalently for the normative sample as in the field-test samples. Conclusions: The BODY-Q scales showed acceptable reliability and validity in the normative sample. The normative values can be used as reference in research and clinical practice in combination with local estimates for parallel analysis and comparison.
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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 | 0.011 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".