Cognitive testing of the Colon Cancer Screening Behaviours Survey with South Asian immigrants in Canada
Bibliographic record
Abstract
The purpose of this study was to cognitively test the Urdu and English language versions of a survey to assess colon cancer screening behaviours among South Asian immigrants in Canada. The Colon Cancer Screening Behaviours Survey was cross-culturally translated and adapted into the Urdu language followed by cognitive interviews using an evidence-informed cross-cultural cognitive interview framework. The cognitive interviews were conducted in English and Urdu in three rounds; a preliminary round, round one, and round two. Two bilingual cognitive interviewers administered interviews in person with South Asian immigrants in Hamilton, Ontario. Scripted verbal and emergent probe techniques were used concurrently with survey item administration. A total of 30 South Asian immigrant participants, 12 English speaking and 18 Urdu speaking completed a cognitive interview. These groups were similar in age, gender, and years of residence in Canada. General design, culture, gender, and translation issues were identified. Revisions were made to improve the survey and the interview protocol was modified for future data collection. The cross-cultural cognitive interview framework led to a systematic and rigorous process of pre-testing and revising the Colon Cancer Screening Behaviours Survey, which may be used to gain insights on beliefs, benefits, facilitators and barriers to colon cancer screening among South Asian immigrants. The study methods and experience may also inform the cross-cultural translation and adaptation and cognitive testing of other survey tools.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".