Interventions to Increase Colorectal Cancer Screening Uptake in Primary Care: A Systematic Review
Bibliographic record
Abstract
OBJECTIVE: We systematically reviewed and summarized previous studies that examined facilitators and barriers to implementing interventions to increase CRCS uptake in primary care practice. METHODS: We searched PubMed, Medline (EBSCO), and CINAHL databases, from the inception of these databases to April 2020. The search strategy combined a set of terms related to facilitators/barriers, intervention implementation, CRCS, and uptake/participation. A priori set inclusion and exclusion criteria were used during both title/abstract screening and full-text screening phases to identify the eligible studies. Quality of the included studies was appraised using quality assessment tools, and data were extracted using a predetermined data extraction tool. We classified facilitators and barriers according to the Consolidated Framework for Implementation Research domains and constructs and identified the common facilitators and barriers looking at how common they were across studies. RESULTS: A total of 12 studies were included in the review. Engagement of the clinic team, leadership team, and partners, clinics' motivation to improve CRCS rates, use of the EMR system, continuous monitoring and feedback system, and having a supportive environment for implementation were the most commonly reported implementation facilitators. Limited time for the clinic team to devote to a new project, challenges in getting accurate, timely data related to CRCS, limited capacity/support to use the EMR system, and disconnect between clinic team members were the most commonly reported implementation barriers. CONCLUSIONS: The synthesized findings improve our understanding of facilitators of and barriers to the implementation of interventions to increase CRCS participation in primary care practice, and inform the customized implementation strategies. Many of the included studies had limited use of rigorous implementation science frameworks to guide their implementation and evaluation, which precludes a comprehensive understanding of the implementation factors specific to CRCS interventions in primary care. Future studies assessing the CRCS intervention implementation factors would benefit from the use of implementation science frameworks.
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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 | 0.016 | 0.070 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.009 | 0.008 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".