Effective interventions to facilitate the uptake of breast, cervical and colorectal cancer screening: an implementation guideline
Bibliographic record
Abstract
BACKGROUND: Appropriate screening may reduce the mortality and morbidity of colorectal, breast, and cervical cancers. Several high-quality systematic reviews and practice guidelines exist to inform the most effective screening options. However, effective implementation strategies are warranted if the full benefits of screening are to be realized. We developed an implementation guideline to answer the question: What interventions have been shown to increase the uptake of cancer screening by individuals, specifically for breast, cervical, and colorectal cancers? METHODS: A guideline panel was established as part of Cancer Care Ontario's Program in Evidence-based Care, and a systematic review of the published literature was conducted. It yielded three foundational systematic reviews and an existing guidance document. We conducted updates of these reviews and searched the literature published between 2004 and 2010. A draft guideline was written that went through two rounds of review. Revisions were made resulting in a final set of guideline recommendations. RESULTS: Sixty-six new studies reflecting 74 comparisons met eligibility criteria. They were generally of poor to moderate quality. Using these and the foundational documents, the panel developed a draft guideline. The draft report was well received in the two rounds of review with mean quality scores above four (on a five-point scale) for each of the items. For most of the interventions considered, there was insufficient evidence to support or refute their effectiveness. However, client reminders, reduction of structural barriers, and provision of provider assessment and feedback were recommended interventions to increase screening for at least two of three cancer sites studied. The final guidelines also provide advice on how the recommendations can be used and future areas for research. CONCLUSION: Using established guideline development methodologies and the AGREE II as our methodological frameworks, we developed an implementation guideline to advise on interventions to increase the rate of breast, cervical and colorectal cancer screening. While advancements have been made in these areas of implementation science, more investigations are warranted.
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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 | 0.071 | 0.129 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.005 | 0.010 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.010 | 0.005 |
| Research integrity | 0.017 | 0.010 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".