Compensatory health motivations and behaviors scale: Development, evaluation, psychometric properties and a preliminary validation
Bibliographic record
Abstract
The "Compensatory Health Beliefs" scale assesses the degree to which one believes that unhealthy behaviours can be compensated through healthier ones. However, no validated scale to assess compensatory weight-related behaviors exists. The study's objective was to develop (Study 1) and validate (Study 2) a questionnaire measuring compensatory health motivations and behaviors (CHMB) and to assess their associations with body mass index (BMI) and psychological weight-related measures. An initial 34-item measure was constructed based on a target sample's (Study 1, n = 158) suggestions and refined based on expert feedback. The measure was then tested in a representative Canadian adult sample (N = 1400, 48.7% male). The sample was stratified by sex and age and then randomly split into two (N = 701 for exploratory factor analysis; N = 699 for confirmatory factor analysis (CFA) cross-validation). Fit indices, standardized Cronbach's alphas and the associations between the CHMB model with cognitive restraint, weight concerns, and BMI were assessed in multiple linear regression models controlling for age and sex. The final CHMB model (n = 17 items) consisted of four subscales: (1) motivation, (2) use on special occasions, (3) general use, (4) compensatory health beliefs. Fit indices (Goodness of Fit Index = 0.922) and Cronbach's alphas were good (α = 0.88). In multiple linear regression models, all CHMB subscales were associated with greater cognitive restraint in eating. Compensatory behavior use on special occasions was associated with greater weight concern (B = 0.12, p < .0001), while general compensatory behavior use was associated with lower weight concern (B = -0.07, p < .05). None of the subscales were associated with BMI. The validated CHMB scale allows for the assessment of compensatory health motivations and behaviors in a Canadian population. Research on whether this scale can predict weight changes and general health is needed.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".