Adopting a multi-level perspective that considers the many interrelated contexts influencing health could make health communication interventions more effective and equitable. However, despite increasing interest in the use of multi-level approaches, multi-level health communication (MLHC) interventions are infrequently utilized. We therefore sought to conduct a modified Delphi study to better understand how researchers conceptualize MLHC interventions and identify opportunities for advancing MLHC work. Communication and health behavior experts were invited to complete two rounds of surveys about the characteristics, benefits, pitfalls, best practices, barriers, and facilitators of MLHC interventions; the role of technology in facilitating MLHC interventions; and ways to advance MLHC intervention research (46 experts completed the first survey, 44 completed both surveys). Survey data were analyzed using a mixed-methods approach. Panelists reached consensus on two components of the proposed definition of MLHC interventions and also put forward a set of best practices for these interventions. Panelists felt that most health intervention research could benefit from a multi-level approach, and generally agreed that MLHC approaches offered certain advantages over single-level approaches. However, they also expressed concern related to the time, cost, and complexity of MLHC interventions. Although panelists felt that technology could potentially support MLHC interventions, they also recognized the potential for technology to exacerbate disparities. Finally, panelists prioritized a set of methodological advances and practical supports that would be needed to facilitate future MLHC intervention research. The results of this study point to several future directions for the field, including advancing how interactions between levels are assessed, increasing the empirical evidence base demonstrating the advantages of MLHC interventions, and identifying best practices for the use of technology. The findings also suggest that researchers may need additional support to overcome the perceived practical challenges of conducting MLHC interventions.
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http://dx.doi.org/10.1093/tbm/ibac068 | DOI Listing |
JMIR Form Res
February 2023
Division of Respiratory and Respiratory Critical Care Medicine, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.
Background: The COVID-19 pandemic has raised global concern, with moderate to severe cases displaying lung inflammation and respiratory failure. Chest x-ray (CXR) imaging is crucial for diagnosis and is usually interpreted by experienced medical specialists. Machine learning has been applied with acceptable accuracy, but computational efficiency has received less attention.
View Article and Find Full Text PDFTransl Behav Med
December 2022
Health Communication and Informatics Research Branch, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD, USA.
Adopting a multi-level perspective that considers the many interrelated contexts influencing health could make health communication interventions more effective and equitable. However, despite increasing interest in the use of multi-level approaches, multi-level health communication (MLHC) interventions are infrequently utilized. We therefore sought to conduct a modified Delphi study to better understand how researchers conceptualize MLHC interventions and identify opportunities for advancing MLHC work.
View Article and Find Full Text PDFLung Cancer
September 2022
Prevention and Early Detection Theme, NIHR Manchester Biomedical Research Centre, Manchester, UK; Division of Infection, Immunity and Respiratory Medicine, University of Manchester, Manchester, UK.
Background: The frequency of lung cancer detection in the Manchester Lung Health Checks (MLHCs), a community-based screening service, was higher than in the National Lung Screening Trial (NLST) over two screening rounds. We aimed to identify the potential reasons for this difference.
Methods: We analyzed individual-level data from NLST and MLHCs, restricting to MLHCs participants who met NLST eligibility criteria.
BMJ
February 2022
Division of Computer Science and Engineering, University of Michigan College of Engineering, Ann Arbor, MI 48109, USA
Objective: To create and validate a simple and transferable machine learning model from electronic health record data to accurately predict clinical deterioration in patients with covid-19 across institutions, through use of a novel paradigm for model development and code sharing.
Design: Retrospective cohort study.
Setting: One US hospital during 2015-21 was used for model training and internal validation.
J Clin Epidemiol
February 2022
Centre for Ethics, University of Toronto, Toronto, Ontario, Canada; Division of Neurosurgery, University of Toronto, Toronto, Ontario, Canada.
Objective: To examine the role of explainability in machine learning for healthcare (MLHC), and its necessity and significance with respect to effective and ethical MLHC application.
Study Design And Setting: This commentary engages with the growing and dynamic corpus of literature on the use of MLHC and artificial intelligence (AI) in medicine, which provide the context for a focused narrative review of arguments presented in favour of and opposition to explainability in MLHC.
Results: We find that concerns regarding explainability are not limited to MLHC, but rather extend to numerous well-validated treatment interventions as well as to human clinical judgment itself.
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