Development of a National Colorectal Cancer Screening Research Agenda: An Initiative of the Canadian Screening for Colorectal Cancer Research Network (CanSCCRN)
Bibliographic record
Abstract
The Canadian Screening for Colorectal Cancer Research Network (CanSCCRN) recently set out to develop a national CRC screening research agenda and identify priority research areas. The specific objectives were to (1) identify evidence gaps relevant to CRC screening and the barriers and facilitators to evidence generation and uptake by CRC screening programs, (2) establish high-priority collaborative research ideas to inform best CRC screening practices, and (3) identify one to two research topics for grant development and submission within 12 to 18 months. Three focus groups were conducted with network members and relevant parties (n = 15) to identify evidence gaps, barriers, and facilitators to evidence generation and uptake. Three workshops were subsequently held to discuss focus group findings and develop an action plan for research. An electronic survey was used to prioritize the evidence gaps to be addressed. Overall, five categories of barriers and six categories of facilitators to evidence uptake and generation were identified, as well as 23 evidence gaps to be addressed. Screening participation, post-polypectomy surveillance, and screening age range were identified as research priority research areas. Adequate resourcing and infrastructure, as well as partnerships with knowledge end users, are integral to addressing these research areas and advancing CRC screening programs in Canada and beyond.
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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 | 0.148 | 0.075 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.014 | 0.004 |
| Scholarly communication | 0.013 | 0.006 |
| Open science | 0.006 | 0.014 |
| Research integrity | 0.005 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".