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Understanding lung cancer screening behaviour using path analysis.

Carter-Harris L, Slaven JE, Monahan PO, Draucker CB, Vode E, Rawl SM. Understanding lung cancer screening behaviour using path analysis. Journal of Medical Screening. 2020 Jun 1; 27(2):105-112.

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Abstract:

OBJECTIVE: Understanding lung cancer screening behaviour is crucial to identifying potentially modifiable factors for future intervention. Qualititative work has explored attitudes and beliefs about lung cancer screening from the perspective of the participant, but the theoretically grounded factors that influence screening-eligible individuals to screen are unknown. We tested an explanatory framework for lung cancer screening participation from the individual's perspective. METHODS: Data were collected as part of a sequential explanatory mixed methods study, the quantitative component of which is reported here. A national purposive sample of 515 screening-eligible participants in the United States was recruited using Facebook-targeted advertisement. Participants completed surveys assessing constructs of the Conceptual Model for Lung Cancer Screening Participation. Path analysis was used to assess the relationships between variables. RESULTS: Path analyses revealed that a clinician recommendation to screen, higher self-efficacy scores, and lower mistrust scores were directly associated with screening participation (p? < 0.05). However, the link between screening behaviour and self-efficacy appeared to be fully mediated by fatalism, lung cancer fear, lung cancer family history, knowledge of lung cancer risk and screening, income, clinician recommendation, and social influence (p? < 0.05). CONCLUSIONS: This study found that medical mistrust, self-efficacy, and clinician recommendation were significant in the decision of whether to screen for lung cancer. These findings offer insight into potentially modifiable targets most appropriate on which to intervene. This understanding is critical to design meaningful clinician- and patient-focused interventions.





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