The Revised Champion’s Health Belief Model Scale: Predictive Validity Among Brazilian Women
Bibliographic record
Abstract
Introduction Mammography screening is provided free of charge for the recommended target population in Brazil but participation rates have remained low, and breast cancer mortality has continued to increase. Thus, it is important to identify factors that are associated with poor participation in mammography screening so that service providers can target health promotion messages and screening programs more effectively. Objective: To evaluate the predictive validity of the Revised Champion’s Health Belief Model scale (RCHBMS) for identifying women at high risk of not adhering to national guidelines for mammography screening in Brazil. Methods: We used a longitudinal survey design with a 1-year follow-up data from 194 women living in northeastern Brazil, in the city of Fortaleza, Ceará, participants completed the RCHBMS at baseline, and mammography uptake was measured 1 year later. Hierarchical logistic regression was used to determine the predictive validity of the RCHBMS for identifying women who had not adhered to recommendations for mammography screening, after accounting for the women’s sociodemographic and clinical characteristics. The sensitivity and specificity of various cut-off points were calculated to determine the optimal cut-off point for identifying women at high risk of not adhering to mammography screening guidelines. Results: Two subscales of the RCHBMS uniquely predicted nonadherence: susceptibility and barriers, along with race and family history of cancer. The total scale score (with barriers reverse coded) was also highly predictive. For our sample, using only the RCHBMS with a cutoff of ≤ 3.67 (out of a total possible range of 1–5) yielded a high sensitivity and specificity for predicting nonadherence. Conclusion: Study findings support the validity and clinical utility of the RCHBM for identifying women at risk of not adhering to national guidelines for mammography screening in Brazil.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".