Impact of Population-Based Screening Programs on Colorectal Cancer Screening Uptake and Predictors in Atlantic Canada: A Repeated Cross-Sectional study
Bibliographic record
Abstract
Abstract Background Colorectal cancer (CRC) poses a significant public health challenge in Canada, with the Atlantic Provinces bearing a particularly high burden. The implementation of population-based colon screening programs is aimed to address this concern. However, limited research exists on the impact of these programs, including their uptake, barriers, and predictors of screening participation. This study aimed to examine impact of the first few years of the Colon Screening programs in the Atlantic Provinces of Canada by assessing changes in screening uptake, barriers, and predictors of screening among eligible population. Methods Employing a repeated cross-sectional design, this study selected and analysed data of a representative sample of respondents from the Atlantic provinces aged 50–74 years from the Canadian Community Health Survey (CCHS) for the years 2010 and 2017. The primary outcome measures were CRC screening rates and changes in predictors of screening uptake between these two data points. The CCHS data provided information on self-reported CRC screening participation and potential predictors such as age, sex, income, and education. Results Proportion of adults aged 50–74 years, up-to-date with CRC screening, increased, on average, from 42–54%, falling short of the national target of 60%. New Brunswick saw the biggest increase in screening prevalence (18%). Participation in the fecal tests increased significantly (19.7% − 32.4%). However, disparities in screening participation persist and, 41% and 50% of people with no screening history deemed the CRC fecal test and endoscopy tests unnecessary, respectively. Age, chronic health conditions, sex, marital status, regular healthcare access and smoking status remained significant predictors of CRC screening uptake. Conclusions This study highlights the need for continued efforts to improve CRC screening uptake in the Atlantic Provinces of Canada. Despite the establishment of screening programs, barriers to access and low awareness persist. Addressing these challenges and monitoring the impact of screening programs is essential to reduce CRC burden in the region and enhance public health outcomes.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".