Knowledge translation strategies to enhance lung cancer screening programme implementation: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Low-dose computed tomography (LDCT) lung cancer screening (LCS) improves outcomes including mortality in clinical trials, but it is unclear whether this evidence is implemented effectively in real-world practice settings. This systematic review explored how knowledge translation (KT) strategies have been used to improve knowledge, decisional confidence and participation in LDCT LCS programmes. METHODS: Literature searches were performed for comparative studies incorporating KT strategies in relation to LDCT LCS. Articles included a KT intervention intended to facilitate knowledge, participation in screening, improve decisional confidence or increase screening uptake. RESULTS: 40 studies were selected for data extraction. Studies emanated from the USA (36), Canada (one), the UK (two) and Japan (one), published between 2014 and 2024. KT interventions reported included 41 implementation strategies targeting staff training, patient and provider education, shared decision-making tools, nurse clinics, navigators, forms, electronic reminders and triggers, data presentation modalities, materials targeting specific populations, and quality improvement tools. Meta-analysis identified significant increase in knowledge of risk (OR 2.87, 95% CI 1.29-6.38), LCS candidacy (OR 2.50, 95% CI 1.51-4.14), risk-benefit knowledge (OR 2.82, 95% CI 1.21-6.58), awareness of screening test (OR 11.91, 9.00-15.76) and increased LCS screening participation (OR 2.24, 95% CI 1.44-3.47) in response to KT strategies. CONCLUSION: This systematic review identified multiple studies addressing the utilisation and effectiveness of implementation science strategies in KT interventions in the context of LCS. These included a broad range of implementation strategies and KT methodologies that were associated with increased LCS knowledge and participation. There is an urgent need to identify effective implementation strategies leading to enhanced knowledge and screening participation amongst at risk individuals in LDCT LCS programmes.
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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 | 0.021 | 0.057 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.016 | 0.032 |
| Bibliometrics | 0.007 | 0.007 |
| 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".