Protection Motivation Theory and Intentions to Receive the COVID-19 Vaccine.

Background. Declared a "public health threat of international concern" by the World Health Organization, the COVID-19 virus has caused the deaths of over half a million individuals in the United States in just the first 23 months after detection. The vaccine has recently been introduced to reduce this public health threat. However, due, in part, to the rapidity with which the vaccine was developed, many individuals display vaccine hesitancy. Purpose. The current study examined the utility of the Protection Motivation Theory of Health (PMT) in predicting intentions to receive the COVID-19 vaccine. Method. One hundred twenty-nine unvaccinated respondents (drawn from an initial sample of 255 participants) completed a survey assessing the components of the PMT and intentions to receive the vaccine. Respondents could also provide an open-ended response regarding any concerns they had with the vaccine. Conclusions. The PMT accounted for 76% of the variance in vaccine intentions. Vulnerability, outcome efficaciousness, and maladaptive response rewards each accounted for unique variance. Open-ended responses reflecting concerns with the vaccine fell into 8 categories, with the most common being concern with the long-term side effects of the vaccine. These results suggest that public health campaigns promoting the vaccine should focus on vulnerability to COVID-19, protective functions of the vaccine, and overcoming what people perceive as benefits of not receiving the vaccine.

Protection motivation unmasked: Applying protection motivation theory to skepticism toward COVID-19 mask and vaccine mandates.

Variants of COVID-19 have sparked controversy regarding mask and/or vaccine mandates in some sectors of the country. Many people hold polarized opinions about such mandates, and it is uncertain what predicts attitudes towards these protective behavior mandates. Through a snow-ball sampling procedure of respondents on social media platforms, this study examined skepticism of 774 respondents toward these mandates as a function of the Protection Motivation Theory (PMT) of health. Hierarchical linear regressions examined Protection Motivation (PM) as a predictor of mask and vaccine mandate skepticism independently and with political party affiliation as a control. PM alone accounted for 76% of the variance in mask mandate skepticism, p < 0.001 and 65% in vaccine mandate skepticism, p < 0.001. When political affiliation was entered (accounting for 28% of the variance in mask mandate skepticism, p < 0.001, and 26% in vaccine mandate skepticism, p < 0.001), PM still accounted for significant percentages of variance in both mask (50%) and vaccine (43%) mandate skepticism, ps < 0.001. Across regressions, perceived severity, outcome efficaciousness, and self-efficacy each directly accounted for unique variance in mask and vaccine mandate skepticism, ps < 0.001; only perceived vulnerability failed to account for unique variance in the regressions, ps > 0.05. Specifically, the more severe participants perceived COVID-19 to be and the greater the perceived efficacy of masks and vaccines preventing the spread of COVID-19, the lower participants' skepticism toward mask and vaccine mandates. Similarly, the higher participants' self-efficacy in wearing masks or receiving the vaccine, the lower their skepticism toward mask and vaccine mandates.

Complete and Resilient Documentation for Operational Medical Environments Leveraging Mobile Hands-free Technology in a Systems Approach: Experimental Study.

BACKGROUND: Prehospitalization documentation is a challenging task and prone to loss of information, as paramedics operate under disruptive environments requiring their constant attention to the patients. OBJECTIVE: The aim of this study is to develop a mobile platform for hands-free prehospitalization documentation to assist first responders in operational medical environments by aggregating all existing solutions for noise resiliency and domain adaptation. METHODS: The platform was built to extract meaningful medical information from the real-time audio streaming at the point of injury and transmit complete documentation to a field hospital prior to patient arrival. To this end, the state-of-the-art automatic speech recognition (ASR) solutions with the following modular improvements were thoroughly explored: noise-resilient ASR, multi-style training, customized lexicon, and speech enhancement. The development of the platform was strictly guided by qualitative research and simulation-based evaluation to address the relevant challenges through progressive improvements at every process step of the end-to-end solution. The primary performance metrics included medical word error rate (WER) in machine-transcribed text output and an F1 score calculated by comparing the autogenerated documentation to manual documentation by physicians. RESULTS: The total number of 15,139 individual words necessary for completing the documentation were identified from all conversations that occurred during the physician-supervised simulation drills. The baseline model presented a suboptimal performance with a WER of 69.85% and an F1 score of 0.611. The noise-resilient ASR, multi-style training, and customized lexicon improved the overall performance; the finalized platform achieved a medical WER of 33.3% and an F1 score of 0.81 when compared to manual documentation. The speech enhancement degraded performance with medical WER increased from 33.3% to 46.33% and the corresponding F1 score decreased from 0.81 to 0.78. All changes in performance were statistically significant (P<.001). CONCLUSIONS: This study presented a fully functional mobile platform for hands-free prehospitalization documentation in operational medical environments and lessons learned from its implementation.