Canadian Landscape Assessment of Colorectal Cancer Screening during the COVID-19 Pandemic
Bibliographic record
Abstract
The COVID-19 pandemic caused disruptions in colorectal cancer (CRC) care by interrupting CRC screening across Canada, posing problems for program participants, patients, and physicians and no clear understanding of how provincial healthcare systems would adapt in the face of another pandemic or shock to the system. A nationwide online survey targeted to members of the National Colorectal Cancer Screening Network (NCCSN) using the SurveyMonkey platform was conducted to gain insight into the impact of the pandemic on CRC screening from March 2020 to March 2022 across all thirteen Canadian jurisdictions. The survey included 25 multiple-choice and free-text questions. Both quantitative and qualitative methods were used to analyze the data using Microsoft Excel and NVivo software. Twenty-one provincial and territorial representatives participated in the survey conducted between 13 May 2022 and 27 October 2022. All jurisdictions (100%) reported decreased screenings, including fecal immunochemical testing (FIT) or Fecal Occult Blood testing (FOBT) procedures, and subsequent diagnostic colonoscopies. The average wait time for colonoscopies due to a positive FIT/FOBT was 76 days. To mitigate the backlog and initiate an effective intervention plan, representatives highlighted some key points, including the importance of prioritizing high-risk patients. Survey results concluded that the COVID-19 pandemic impacted CRC screening across Canada. This landscape assessment can help inform intervention measures and policy-related solutions to create greater resilience for CRC screening in provincial and territorial healthcare systems.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".