Interventions Designed to Increase the Uptake of Lung Cancer Screening: An Equity-Oriented Scoping Review
Bibliographic record
Abstract
Introduction: Participation in lung cancer screening (LCS) is lower in populations with the highest burden of lung cancer risk (through the social patterning of smoking behavior) and lowest levels of health care utilization (through structurally inaccessible care) leading to a widening of health inequities. Methods: We conducted a scoping review using the Arksey and O'Malley methodological framework to inform equitable access to LCS by illuminating knowledge and implementation gaps in interventions designed to increase the uptake of LCS. We comprehensively searched for LCS interventions (Ovid Medline, Excerpta Medica database, the Cochrane Library, Cumulative Index to Nursing and Allied Health Literature, and Scopus from 2000 to June 22, 2021) and included peer-reviewed articles and gray literature published in the English language that describe an intervention designed to increase the uptake of LCS, charted data using our previously published tool and conduced a health equity analysis to determine the intended-unintended and positive-negative outcomes of the interventions for populations experiencing the greatest inequities. Results: Our search yielded 3572 peer-reviewed articles and 54,292 pieces of gray literature. Ultimately, we included 35 peer-reviewed articles and one gray literature. The interventions occurred in the United States, United Kingdom, Japan, and Italy, focusing on shared decision-making, the use of electronic health records as reminders, patient navigation, community-based campaigns, and mobile computed tomography scanners. We developed an equity-oriented LCS framework and mapped the dimensions and outcomes of the interventions on access to LCS on the basis of approachability, acceptability, availability, affordability, and appropriateness of the intervention. No intervention was mapped across all five dimensions. Most notably, knowledge and implementation gaps were identified in dimensions of acceptability, availability, and affordability. Conclusions: Interventions that were most effective in improving access to LCS targeted priority populations, raised community-level awareness, tailored materials for sociocultural acceptability, did not depend on prior patient engagement/registration with the health care system, proactively considered costs related to participation, and enhanced utilization through informed decision-making.
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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 | 0.042 | 0.147 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".