背景:建筑和护理是关键行业。尽管这两种职业都涉及身心要求高的工作,COVID-19大流行期间工人面临的风险还没有得到很好的理解。与建筑工人相比,护士(年轻人和老年人)更容易受到倦怠和压力的不良影响,可能是由于COVID-19大流行期间工作需求加快和护士压力增加。在这项研究中,我们使用先进的自然语言处理技术分析了一个大型社交媒体数据集,以探索COVID-19大流行之前和期间两个行业工人的精神状态指标.
目的:此社交媒体分析旨在通过比较年轻和年长的建筑工人和护士的推文,以了解由于工作健康和安全问题而对其心理健康造成的任何潜在风险,从而填补知识空白。
方法:我们分析了年轻和年长(年龄<45岁vs>45岁)的建筑工人和护士在Twitter上发布的1,505,638条推文(随后更名为X)。研究期为54个月,从2018年1月至2022年6月,这相当于2020年3月11日世界卫生组织宣布COVID-19为全球大流行之前约27个月和之后27个月。使用大数据分析和计算语言分析对推文进行了分析。
结果:文本分析显示,护士更多地使用与职业倦怠相关的标签和关键词(包括单字母和双字母),健康问题,与建筑工人相比,心理健康。COVID-19大流行对护士的推文产生了显著影响,这在年轻护士中尤其明显。关于健康和幸福的推文包含更多的第一人称单数代词和影响词,与健康相关的推文包含更多影响词。情绪分析显示,总的来说,护士在推文中的积极情绪比例高于建筑工人。然而,在COVID-19大流行期间,这种情况发生了明显变化。自2020年初以来,情绪发生了变化,负面情绪主导了护士的推文。在建筑工人的推文中没有观察到这种交叉。
结论:社交媒体分析显示,年轻护士的语言使用模式与经历倦怠和压力的不良影响的人一致。年长的建筑工人比年轻工人有更多的负面情绪,他们更专注于社交和娱乐活动的交流,而不是工作。更广泛地说,这些发现证明了社交媒体启用的大型数据集的实用性,以了解目标人群的福祉,尤其是在社会变革迅速的时期。
BACKGROUND: Construction and nursing are critical industries. Although both careers involve physically and mentally demanding work, the risks to workers during the COVID-19 pandemic are not well understood. Nurses (both younger and older) are more likely to experience the ill effects of burnout and stress than construction workers, likely due to accelerated work demands and increased pressure on nurses during the COVID-19 pandemic. In this study, we analyzed a large social media data set using advanced natural language processing techniques to explore indicators of the mental status of workers across both industries before and during the COVID-19 pandemic.
OBJECTIVE: This social media analysis aims to fill a knowledge gap by comparing the tweets of younger and older construction workers and nurses to obtain insights into any potential risks to their mental health due to work health and safety issues.
METHODS: We analyzed 1,505,638 tweets published on Twitter (subsequently rebranded as X) by younger and older (aged <45 vs >45 years) construction workers and nurses. The study period spanned 54 months, from January 2018 to June 2022, which equates to approximately 27 months before and 27 months after the World Health Organization declared COVID-19 a global pandemic on March 11, 2020. The tweets were analyzed using big data analytics and computational linguistic analyses.
RESULTS: Text analyses revealed that nurses made greater use of hashtags and keywords (both monograms and bigrams) associated with burnout, health issues, and mental health compared to construction workers. The COVID-19 pandemic had a pronounced effect on nurses\' tweets, and this was especially noticeable in younger nurses. Tweets about health and well-being contained more first-person singular pronouns and affect words, and health-related tweets contained more affect words. Sentiment analyses revealed that, overall, nurses had a higher proportion of positive sentiment in their tweets than construction workers. However, this changed markedly during the COVID-19 pandemic. Since early 2020, sentiment switched, and negative sentiment dominated the tweets of nurses. No such crossover was observed in the tweets of construction workers.
CONCLUSIONS: The social media analysis revealed that younger nurses had language use patterns consistent with someone experiencing the ill effects of burnout and stress. Older construction workers had more negative sentiments than younger workers, who were more focused on communicating about social and recreational activities rather than work matters. More broadly, these findings demonstrate the utility of large data sets enabled by social media to understand the well-being of target populations, especially during times of rapid societal change.