Interventions designed to increase the uptake of lung cancer screening and implications for priority populations: a scoping review protocol
Bibliographic record
Abstract
BACKGROUND: When designing any health intervention, it is important to respond to the unequal determinants of health by prioritising the allocation of resources and tailoring interventions based on the disproportionate burden of illness. This approach, called the targeting of priority populations, can prevent a widening of health inequities, particularly those inequities which can be further widened by differences in the uptake of an intervention. The objective of this scoping review is to describe intervention(s) designed to increase the uptake of lung cancer screening, including the health impact on priority populations and to describe knowledge and implementation gaps to inform the design of equitable lung cancer screening. METHODS: We will conduct a scoping review following the methodological framework developed by Arksey and O'Malley. We will conduct comprehensive searches for lung cancer screening promotion interventions in Ovid Medline, Embase, the Cochrane Library, Cumulative Index to Nursing & Allied Health (CINAHL) and Scopus. We will include published English language peer-reviewed and grey literature published between January 2000 and 2020 that describe an intervention designed to increase the uptake of low-dose CT (LDCT) lung cancer screening in the Organization for Economic Cooperation and Development countries. Articles not in English or not describing LDCT will be excluded. Three authors will review retrieved literature in three steps: title, abstract and then full text. Three additional authors will review discrepancies. Authors will extract data from full-text papers into a chart adapted from the Template for Intervention Description and Republication checklist, the Consolidated Standards of Reporting Trials and a Health Equity Impact Assessment tool. Findings will be presented using a narrative synthesis. ETHICS AND DISSEMINATION: The knowledge synthesised will be used to inform the equitable design of lung cancer screening and disseminated through conferences, publications and shared with relevant partners. The study does not require research ethics approval as literature is available online.
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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 | 0.126 | 0.100 |
| Meta-epidemiology (narrow) | 0.006 | 0.007 |
| Meta-epidemiology (broad) | 0.012 | 0.014 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.012 | 0.008 |
| Insufficient payload (model declined to judge) | 0.089 | 0.021 |
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".