The colon cancer screening behaviours survey for South Asians: a pilot study of feasibility and psychometric evaluation
Bibliographic record
Abstract
PURPOSE: The purpose of the study was to pilot test the English and Urdu version of the Colon Cancer Screening Behaviours Survey among South Asians in Canada. The first objective was to evaluate feasibility of administration, data collection using computer assisted personal interviewing software on a tablet, and response burden. The second objective was to examine the prevalence of colorectal cancer screening among South Asians and evaluate the psychometric properties of sub-scales in the survey. METHODS: Purposive, network and snowball sampling were used to recruit participants for this cross-sectional study. Interviewer-led administration of the Colon Cancer Screening Behaviours Survey was conducted across two cities in Ontario, Canada. Qualitative data analysis assessed feasibility; and sub-scales were evaluated through principal component analysis, item-scale correlations, and construct validity using multiple linear and logistic regression. RESULTS: A total of 328 South Asians participated, 47% Urdu speaking, and 53% English speaking. There was a 23% refusal rate to participate. Feasibility identified: (1) successful recruitment despite reasons for refusal; (2) problematic items and response categories; and (3) computer/tablet limitations. Principal component analysis identified 14 components that explained 68.7% of total variance; 34 items were retained after factor analysis. Internal consistency of 4 scales ranged from 0.79-0.91. There were significant differences in perceived barriers scale scores (- 12.21; 95% CI, - 17.13 to - 7.28; p < 0.0001) between those who participated and those who did not participate in screening. No association was found with years of residence and uptake of screening after adjustment (OR 0.91 (0.46-1.79), p = 0.783). CONCLUSIONS: Recruitment and data collection methods are feasible among South Asians if functionality of the tablet selected is improved. The Colon Cancer Screening Behaviours Survey was finalized and retained items in sub-scales demonstrated good psychometric properties to assess behaviours for colon cancer screening among South Asians in Canada. The interviewer-led survey may be used by public health, cancer care or other health practitioners to describe or predict colorectal cancer screening behaviours among South Asians in similar settings or adapted and tested in other contexts.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".