RESUMO
Introduction: As useful tools for clinical decision-making, diagnostic tests require careful interpretation in order to prevent underdiagnosis, overdiagnosis or misdiagnosis. The aim of this study was to explore primary care practitioners' understanding and interpretation of the probability of disease before and after test results for six common clinical scenarios. Methods: This cross-sectional study was conducted with 414 family physicians who were working at primary care in Istanbul via face-to-face interviews held between November 2021 and March 2022. The participants were asked to estimate the probability of diagnosis in six clinical scenarios provided to them. Clinical scenarios were about three cancer screening cases (breast, cervical and colorectal), and three infectious disease cases (pneumonia, urinary tract infection, and COVID-19). For each scenario participants estimated the probability of the diagnosis before application of a diagnostic test, after a positive test result, and after a negative test result. Their estimates were compared with the true answers derived from relevant guidelines. Results: For all scenarios, physicians' estimates were significantly higher than the scientific evidence range. The minimum overestimation was positive test result for COVID-19 and maximum was pre-test case for cervical cancer. In the hypothetical control question for prevalence and test accuracy, physicians estimated disease probability as 95.0% for a positive test result and 5.0% for a negative test result while the correct answers were 2.0 and 0%, respectively (p < 0.001). Discussion: Comparing the scientific evidence, overestimation in all diagnostic scenarios, regardless of if the disease is an acute infection or a cancer, may indicate that the probabilistic approach is not conducted by the family physicians. To prevent inaccurate interpretation of the tests that may lead to incorrect or unnecessary treatments with adverse consequences, evidence-based decision-making capacity must be strengthened.
RESUMO
Twitter is a useful source for detecting anti-vaccine content due to the increasing prevalence of these arguments on social media. We aimed to identify the prominent themes about vaccine hesitancy and refusal on social media posts in Turkish during the COVID-19 pandemic. In this qualitative study, we collected public tweets (n = 551,245) that contained a vaccine-related keyword and had been published between 9 December 2020 and 8 January 2021 through the Twitter API. A random sample of tweets (n = 1041) was selected and analyzed by four researchers with the content analysis method. We found that 90.5% of the tweets were about vaccines, 22.6% (n = 213) of the tweets mentioned at least one COVID-19 vaccine by name, and the most frequently mentioned COVID-19 vaccine was CoronaVac (51.2%). We found that 22.0% (n = 207) of the tweets included at least one anti-vaccination theme. Poor scientific processes (21.7%), conspiracy theories (16.4%), and suspicions towards manufacturers (15.5%) were the most frequently mentioned themes. The most co-occurring themes were "poor scientific process" with "suspicion towards manufacturers" (n = 9), and "suspicion towards health authorities" (n = 5). This study may be helpful for health managers, assisting them to identify the major concerns of the population and organize preventive measures through the significant role of social media in early spread of information about vaccine hesitancy and anti-vaccination attitudes.
