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Title: Analysis of Direct-To-Consumer Healthcare Service Advertisements on Television: An Application of the Patient Expectation Framework
Direct-to-consumer advertisements for healthcare services constitute a rare channel of public communication where consumers see and hear directly from their local providers and healthcare organizations. Although spending on these advertisements has increased drastically during the past decades, research on their content and effects remains rare. To fill this gap, we analyzed primetime television advertisements for healthcare services directly targeting consumers. The advertisements were collected from the two largest media markets in Nevada for one month. In total, 795 advertisements were identified, and 106 of them were non-duplicates. Analysis revealed that the advertisements focused on patients’ good health outcomes by showing them smiling, going out and about, having fun with others, and enjoying rigorous physical activities. On the other hand, the advertisements focused less on the providers. Although the advertisements often showed providers in clinical settings, basic information about their professional degrees was often missing. Mentions of providers’ other qualifications and professional experiences were even scarcer. Also, a substantial number of advertisements failed to show providers interacting with patients. Additional analysis of patient and provider characteristics revealed under-representation of racial or ethnic minority and older adult patients. Representation of women and minorities as providers was even more uncommon. We discussed the more » implications of these findings from the perspective of patient expectation and made suggestions to help providers improve their direct-to-consumer advertisements. « less
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Health Communication
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National Science Foundation
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  5. Obeid, Iyad ; Selesnick, Ivan ; Picone, Joseph (Ed.)
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A summary of the labels being used to annotate the data is shown in Table 2. Certain standards are put into place to optimize the annotation process while not sacrificing consistency. Due to the nature of EEG recordings, some recordsmore »start off with a segment of calibration. This portion of the EEG is instantly recognizable and transitions from what resembles lead artifact to a flat line on all the channels. For the sake of seizure annotation, the calibration is ignored, and no time is wasted on it. During the identification of seizure events, a hard “3 second rule” is used to determine whether two events should be combined into a single larger event. This greatly reduces the time that it takes to annotate a file with multiple events occurring in succession. In addition to the required minimum 3 second gap between seizures, part of our standard dictates that no seizure less than 3 seconds be annotated. Although there is no universally accepted definition for how long a seizure must be, we find that it is difficult to discern with confidence between burst suppression or other morphologically similar impressions when the event is only a couple seconds long. This is due to several reasons, the most notable being the lack of evolution which is oftentimes crucial for the determination of a seizure. After the EEG files have been triaged, a team of annotators at NEDC is provided with the files to begin data annotation. An example of an annotation is shown in Figure 1. A summary of the workflow for our annotation process is shown in Figure 2. Several passes are performed over the data to ensure the annotations are accurate. Each file undergoes three passes to ensure that no seizures were missed or misidentified. The first pass of TUSZ involves identifying which files contain seizures and annotating them using our annotation tool. 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