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BACKGROUND: The current nursing shortage is expected to worsen in the coming years. While this shortage has in part been attributed to challenging working conditions, which were exacerbated during the COVID-19 pandemic, there are other factors. Although recruiting a more diverse health care workforce might help to reduce this shortage, little is known about how to do so effectively. This study explored how online images of health care workers vary by gender and skin tone and considered how those representations might impact recruitment. METHODS: This was a prospective pragmatic study. Using a popular image search engine, we obtained and evaluated images (photographs, drawings, and cartoons) of people representing different genders and a range of skin tones. The search terms used were student, nurse, and physician. The search took place during a single day and stopped once 50 images for each term had been retrieved. Images were included if they depicted at least one human. Each image was evaluated independently by two raters regarding gender (male or female) and skin tone (using a version of the 10-point Monk Skin Tone Scale). RESULTS: The final sample comprised 150 images that depicted 259 faces. Compared with the general population, nurses were overrepresented as being female (84.1%) and having lighter skin tones (85.2%). Physicians were overrepresented as being male (58.7%) and having lighter skin tones (63.5%). Compared with physicians or students, nurses were more likely to be represented as female and as having lighter skin tones. CONCLUSIONS: The findings show a significant lack of diversity in gender and skin tone regarding how nurses are depicted in online images found via a popular image search engine. This lack of diversity may discourage people in some population groups from entering the health care field and could further contribute to the nursing shortage.

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Global healthcare systems continue to be challenged by the COVID-19 pandemic, and there is a need for clinical assays that can both help to optimize resource allocation and accelerate the development and evaluation of new therapies. Here, we present a multiplex proteomic panel assay for the assessment of disease severity and outcome prediction in COVID-19. The assay quantifies 50 peptides derived from 30 COVID-19 severity markers in a single measurement using analytical flow rate liquid chromatography and multiple reaction monitoring (LC-MRM), on equipment that is broadly available in routine and regulated analytical laboratories. We demonstrate accurate classification of COVID-19 severity in patients from two cohorts. Furthermore, the assay outperforms established risk assessments such as SOFA and APACHE II in predicting survival in a longitudinal COVID-19 cohort. The prognostic value implies its use for support of clinical decisions in settings with overstrained healthcare resources e.g. to optimally allocate resources to severely ill individuals with high chance of survival. It can furthermore be helpful for monitoring of novel therapies in clinical trials.