The Chinese Version of the Breast Cancer Literacy Assessment Tool: Translation, Adaptation, and Validation Study
Bibliographic record
Abstract
Background Breast cancer is the most common cancer among Chinese women, with an age-standardized prevalence of 21.6 cases per 100,000 women. Limited cancer health literacy reduces females’ ability to engage in cancer prevention and detection. It is necessary to assess Chinese women’s breast cancer literacy to deliver targeted interventions and effective education. However, there is no Breast Cancer Literacy Assessment Tool (B-CLAT) available in China currently. Objective This study aimed to translate and linguistically and culturally adapt the B-CLAT into a simplified-Chinese version (C-B-CLAT), and then validate its psychometric properties by administering it to Chinese college students. Methods First, we translated the B-CLAT into a simplified-Chinese version and verified its validity and reliability using rigorous translation and validation guidelines proposed in previous studies. We then evaluated the psychometric properties among 50 female participants with a mean age of 19.62 (SD 1.31) years recruited from Nantong University, China. Results Items 1, 6, 8, 9, 10, 16, 17, 20, 21, 22, 23, 24, 25, 26, 29, and 30 were deleted to increase the relevant subscale internal consistency. Items 3, 12, 13, 14, 18, 20, 27, and 31 were deleted due to their Cronbach α being lower than .5 in the test-retest analysis. After deletion, the internal consistency of the entire scale was fair with α=.607. The prevention and control subscale had the highest internal consistency with α=.730, followed by the screening and knowledge subscale with α=.509, while the awareness subscale had the lowest internal consistency with α=.224. The intraclass correlation coefficient for the C-B-CLAT (items 2, 4, 5, 7, 11, 15, 28, 32, 33, and 34) was fair to excellent (odds ratio [OR] 0.88, 95% CI 0.503-0.808). The values of Cronbach α for items 2, 4, 5, 7, 11, 15, 28, 32, 33, and 34 ranged from .499 to .806, and the α value for the C-B-CLAT was .607. This indicates fair test-retest reliability. The mean difference in the C-B-CLAT scores between stage 1 and stage 2 was 0.47 (OR 0.67, 95% CI −0.53 to 1.47), which was not significantly different from zero (t48=0.945; P=.35). This result implies that the C-B-CLAT produced the same scores at stage 1 and stage 2 on average, thus showing good agreement in the C-B-CLAT scores between stage 1 and stage 2. The SD of the difference was 3.48. The 95% limits of agreement were −6.34 to 7.28. Conclusions We developed a simplified-Chinese version of the B-CLAT through translation and adaptation. Psychometric properties testing has proven this version valid and reliable for assessing breast cancer literacy among Chinese college students.
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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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".