Screening behaviours in relatives of Ontario colorectal cancer patients: a social-ecological approach
Bibliographic record
Abstract
Purpose of study. To identify whether screening decisions in relatives of colorectal cancer (CRC) patients are influenced by family members, friends, medical advice, or public awareness campaigns. Methods. Eligible relatives of CRC patients participating in a population-based familial CRC registry were invited to take part in a brief telephone interview. The variables assessed in the telephone interview were derived from an adaptation of the Social-ecological framework, in order to examine potential influences of family members, medical professionals and social contacts. Those who had undergone screening were compared with those who had not, and subjects undergoing diagnostic tests were excluded. Results. Ninety-three percent of the 609 subjects invited replied; 420 (69%) agreed to the interview, and 368 met the final inclusion criteria. Logistic regression analyses demonstrated that having encouragement from a family physician, having few barriers to screening, having 3 or more family members with CRC, having encouragement from family members, having been given advice from a surgeon, and having ever discussed CRC screening with social groups were associated with having ever been screened. Perceived risk, advice from family members, exposure to advertising/public awareness campaigns, and confidence in the health care system were not associated with screening. Non-screeners frequently cited being asymptomatic as the main reason for not screening, while screeners indicated that their family history of CRC was the most important factor in deciding to be screened. Conclusions. The social-ecological framework provides a good explanatory model for CRC screening in increased-risk relatives. Variables from each of the individual-, family-, physician-, and societal-levels were associated with screening. Encouragement from physicians and having few barriers to screening were the strongest correlates of CRC screening behaviour in this group of increased-risk individuals. These findings may be useful in designing interventions aimed at increasing screening uptake, perhaps emphasising the role of the family physician. Educational interventions that focus on the nature of asymptomatic screening should also be considered.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".