Barriers and facilitators to colonoscopy participation for colorectal cancer screening: a rapid review
Bibliographic record
Abstract
Colonoscopy uptake is low despite its effectiveness at detecting colorectal cancer (CRC)-related abnormalities at early stages. There is limited synthesized information regarding the facilitators and barriers to colonoscopy utilization for colorectal cancer screening (CRCS) utilizing qualitative, quantitative, and mixed-methods studies. This study aimed to systematically synthesize facilitators and barriers to colonoscopy screening for CRC from the patient, clinician, and health system perspective. Our search involved four databases (PubMed, MEDLINE (Ovid), Embase (EBSCO), and CINAHL) to find studies published up to May 2023. Abstract screening was completed in duplicate by two independent reviewers until 85% agreement was reached, after which a single reviewer completed the remaining abstract review as well as full-text review. We used a predetermined data extraction tool and completed quality assessments of included studies using standardized tools for each study design. We used narrative synthesis to summarize findings and map our findings to the updated Consolidated Framework for Implementation Research (CFIR2.0). Altogether 71 studies were included. The top facilitators were perception of higher risk, clinician recommendation, considering colonoscopy as an accurate/effective test, social/family support, and knowledge of CRCS. The top barriers were perceived pain associated with the procedure, lack of provider recommendation, lack of knowledge and awareness of colonoscopy and CRC screening, perceived discomfort with the procedure, and perceived embarrassment/humiliation. Strategies to improve colonoscopy uptake must focus on reducing fear of pain and fear of the procedure, improving knowledge and awareness regarding screening among patients, and promoting patient and provider communication.
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 | 0.035 | 0.108 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.017 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".