Introduction: The purpose of this research is to gather, collate, and identify key factors commonly studied in localized prostate cancer (LPC) treatment decision-making in Canada and the U.S.Methods: This scoping review uses five databases (Medline, EMBASE, CINAHL, AMED, and PsycInfo) to identify relevant articles using a list of inclusion and exclusion criteria applied by two reviewers. A list of topics describing the themes of the articles was extracted and key factors were identified using principal component analysis (PCA). A word cloud of titles and abstracts of the relevant articles was created to identify complementary results to the PCA.Results: This review identified 77 relevant articles describing 32 topics related to LPC treatment decision-making. The PCA grouped these 32 topics into five key factors commonly studied in LPC treatment decision-making: 1) treatment type; 2) socioeconomic/demographic characteristics; 3) personal reasons for treatment choice; 4) psychology of treatment decision experience; and 5) level of involvement in the decision-making process. The word cloud identified common phrases that were complementary to the factors identified through the PCA.Conclusions: This research identifies several possible factors impacting LPC treatment decision-making. Further research needs to be completed to determine the impact that these factors have in the LPC treatment decision-making experience.
In this article, we aim to expand situational analysis (SA), oriented by complex adaptive systems (CAS), by adding the dimension of directionality over time to positional maps. This addition furthers the analytic utility of SA and can aid researchers in identifying areas for transformative action regarding social justice and health equity issues. Adding directionality over time to positional maps pushes researchers to explore how positions move, evolve, and how they could continue to develop. Analyzing these elements expands the analytic utility of positional maps as researchers abductively analyze explicit connections between theorized antecedents, current conditions, and potential futures within a CAS to understand positional movements. The purpose of this analysis is not as a predictive tool but as a tool in identifying potential actionable areas for interventions while further grounding SA in its Foucauldian and Straussian theoretical roots. We use an ongoing public health project in the Ngorongoro Conservation Area, Tanzania, to demonstrate how a researcher can apply directionality over time to positional maps.
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