The compensatory health beliefs scale: psychometric properties of a cross-culturally adapted scale for use in The Netherlands
Bibliographic record
Abstract
This study assesses the psychometric properties of a measuring scale for compensatory health beliefs (CHBs), culturally adapted for use in the Dutch context. CHBs refer to the idea that people can compensate for unhealthy (mostly pleasant) behaviours with healthy behaviours, e.g. 'It is OK to eat a chocolate bar, because I am going to the gym tonight'. We are critical towards such beliefs as they may also be an excuse to justify unhealthy behaviours. Before such effects can be studied, an appropriate tool to measure CHBs must be developed. We adapted a Canadian scale, consisting of four factors relating to beliefs about substance use, eating/sleeping habits, stress and weight regulation, translating it according to guidelines for cross-cultural adaptation and testing it among 145 Dutch students. Factor analysis showed that the structure was not entirely identical in the Dutch context, and the internal consistency of the four subscales was also low. The overall scale showed a high internal consistency (alpha = 0.78), indicating the existence of an underlying construct, and a high Pearson correlation between the first and second measurements (r = 0.82), showing good stability. We recommend using the overall scale and further studying its reliability among other subgroups as well as its validity.
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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 | 0.004 | 0.013 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".