Fecal occult blood testing: people in Ontario are unaware of it and not ready for it.
Bibliographic record
Abstract
OBJECTIVE: To determine factors that influence awareness of, and readiness to undergo, fecal occult blood testing (FOBT) for colorectal cancer (CRC) screening. DESIGN: Validated survey designed to ascertain respondents' stages of decision making regarding CRC screening using FOBT. SETTING: Ontario. PARTICIPANTS: A total of 1013 people 50 years old and older drawn from all regions of the province using a random-digit dialing telephone protocol. MAIN OUTCOME MEASURES: Awareness of FOBT and readiness to undergo it for screening for CRC. RESULTS: Response rate was 69%. Results indicated that 54% of women and 45% of men had "heard of" FOBT, and 26% of women and 17% of men had heard of it but were still "not considering" FOBT screening. Only 17% of all respondents had "decided to have" FOBT screening. Demographic factors associated with having heard of FOBT were female sex, completion of college or higher education, and being married or living as married. Demographic factors associated with active consideration of FOBT among those who reported awareness of it were male sex and being married or living as married. CONCLUSION: Many people seemed uninformed about FOBT and not ready to undertake this type of screening. Results of this survey could help guide strategies and develop programs to make eligible people aware of CRC screening using FOBT and to motivate them to undergo testing.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".