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Record W4318954097 · doi:10.1016/j.jtocrr.2023.100469

Interventions Designed to Increase the Uptake of Lung Cancer Screening: An Equity-Oriented Scoping Review

2023· article· en· W4318954097 on OpenAlex
Ambreen Sayani, Muhanad Ahmed Ali, Pooja Dey, Ann Marie Corrado, Carolyn Ziegler, Erika Nicholson, Aïsha Lofters

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueJTO Clinical and Research Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicLung Cancer Diagnosis and Treatment
Canadian institutionsInstitute for Clinical Evaluative SciencesCanadian Partnership Against CancerPublic Health OntarioUniversity of TorontoWomen's College HospitalSt. Michael's Hospital
FundersCanadian Institutes of Health ResearchPartenariat Canadien Contre Le CancerWomen's College Hospital
KeywordsPsychological interventionGrey literatureScopusMEDLINEMedicineHealth equityFamily medicineHealth careGerontologyNursingPublic healthPolitical science

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.376
GPT teacher head0.620
Teacher spread0.244 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it