A systematic review of the quality of psychometric evidence supporting the use of an obesity‐specific quality of life measure for use with persons who have class III obesity
Bibliographic record
Abstract
With global obesity rates at 42%, there is a need for high-quality outcome measures that capture important aspects of quality of life for persons with obesity. The aim of this paper was to systematically review and critique the psychometric properties and utility of the impact of weight on quality of life-lite (IWQOL-Lite) for use with persons who have class III obesity. Databases were searched for articles that addressed obesity-specific quality of life. A critical appraisal of the psychometric properties of the IWQOL-Lite and connection to a quality of life conceptual framework was completed. Raters used a standardized data extraction and quality appraisal form to guide evidence extraction. Two articles that reviewed obesity-specific quality of life measures were found; none were based on a systematic review. Six articles on the IWQOL-Lite met the criteria for critical appraisal using guidelines. The mean quality score for these articles was 59.2%. Concepts measured were consistent with the biopsychosocial concept of health defined by the World Health Organization. There is limited but consistent evidence that the IWQOL-Lite is a reliable, valid and responsive outcome measure that can be used to assess disease-specific quality of life in persons with class III obesity.
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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 | 0.031 | 0.181 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.006 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".