What implementation interventions increase cancer screening rates? a systematic review
Bibliographic record
Abstract
BACKGROUND: Appropriate screening may reduce the mortality and morbidity of colorectal, breast, and cervical cancers. However, effective implementation strategies are warranted if the full benefits of screening are to be realized. As part of a larger agenda to create an implementation guideline, we conducted a systematic review to evaluate interventions designed to increase the rate of breast, cervical, and colorectal cancer (CRC) screening. The interventions considered were: client reminders, client incentives, mass media, small media, group education, one-on-one education, reduction in structural barriers, reduction in out-of-pocket costs, provider assessment and feedback interventions, and provider incentives. Our primary outcome, screening completion, was calculated as the overall median post-intervention absolute percentage point (PP) change in completed screening tests. METHODS: Our first step was to conduct an iterative scoping review in the research area. This yielded three relevant high-quality systematic reviews. Serving as our evidentiary foundation, we conducted a formal update. Randomized controlled trials and cluster randomized controlled trials, published between 2004 and 2010, were searched in MEDLINE, EMBASE and PSYCHinfo. RESULTS: The update yielded 66 studies new eligible studies with 74 comparisons. The new studies ranged considerably in quality. Client reminders, small media, and provider audit and feedback appear to be effective interventions to increase the uptake of screening for three cancers. One-on-one education and reduction of structural barriers also appears effective, but their roles with CRC and cervical screening, respectively, are less established. More study is required to assess client incentives, mass media, group education, reduction of out-of-pocket costs, and provider incentive interventions. CONCLUSION: The new evidence generally aligns with the evidence and conclusions from the original systematic reviews. This review served as the evidentiary foundation for an implementation guideline. Poor reporting, lack of precision and consistency in defining operational elements, and insufficient consideration of context and differences among populations are areas for additional research.
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 | 0.028 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.012 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".