Weight Bias: A Systematic Review of Characteristics and Psychometric Properties of Self-Report Questionnaires
Bibliographic record
Abstract
BACKGROUND: People living with overweight and obesity often experience weight-based stigmatization. Investigations of the prevalence and correlates of weight bias and evaluation of weight bias reduction interventions depend upon psychometrically-sound measurement. Our paper is the first to comprehensively evaluate the psychometric properties, use of people-first language within items, and suitability for use with various populations of available self-report measures of weight bias. METHODS: We searched five electronic databases to identify English-language self-report questionnaires of weight bias. We rated each questionnaire's psychometric properties based on initial validation reports and subsequent use, and examined item language. RESULTS: Our systematic review identified 40 original self-report questionnaires. Most questionnaires were brief, demonstrated adequate internal consistency, and tapped key cognitive and affective dimensions of weight bias such as stereotypes and blaming. Current psychometric evidence is incomplete for many questionnaires, particularly with regard to the properties of test-retest reliability, sensitivity to change as well as discriminant and structural validity. Most questionnaires were developed prior to debate surrounding terminology preferences, and do not employ people-first language in the items administered to participants. CONCLUSIONS: We provide information and recommendations for clinicians and researchers in selecting psychometrically sound measures of weight bias for various purposes and populations, and discuss future directions to improve measurement of this construct.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.047 | 0.207 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.009 |
| Bibliometrics | 0.018 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".