Using Twitter to Understand COVID-19 Vaccine-Related Ageism During the Pandemic.

BACKGROUND AND OBJECTIVES: During the rollout of coronavirus 2019 (COVID-19) vaccines, older adults in high-income countries were often prioritized for inoculation in efforts to reduce COVID-19-related mortality. However, this prioritization may have contributed to intergenerational tensions and ageism, particularly with the limited supply of COVID-19 vaccines. This study examines Twitter discourse to understand vaccine-related ageism during the COVID-19 pandemic to inform future vaccination policies and practices to reduce ageism. RESEARCH DESIGN AND METHODS: We collected 1,369 relevant tweets on Twitter using the Twint application in Python from December 8, 2020, to December 31, 2021. Tweets were analyzed using thematic analysis, and steps were taken to ensure rigor. RESULTS: Our research identified four main themes including (a) blame and hostility: "It's all their fault"; (b) incompetence and misinformation: "clueless boomer"; (c) ageist political slander; and (d) combatting ageism: advocacy and accessibility. DISCUSSION AND IMPLICATIONS: Our findings exposed issues of victim-blaming, hate speech, pejorative content, and ageist political slander that is deepening the divide of intergenerational conflict. Although a subset of tweets countered negative outcomes and demonstrated intergenerational solidarity, our findings suggest that ageism may have contributed to COVID-19 vaccine hesitancy among older adults. Consequently, urgent action is needed to counter vaccine misinformation, prohibit aggressive messaging, and promote intergenerational unity during the COVID-19 pandemic and beyond.

The Influence of the Health Belief Model on the Decision to Get the COVID-19 Vaccine: An International Survey Study of College Students.

To better prepare for a potential future pandemic, it is important to investigate factors that influenced responses to the recent COVID-19 pandemic. The aim of the present study was to investigate factors that influenced the decision to get the COVID-19 vaccine. The COVID-19 pandemic has affected almost everyone throughout the world. Vaccines are a significant factor in managing a pandemic. As vaccines are developed, governments develop vaccine roll-out plans. Unfortunately, vaccine hesitancies can slow the implementation of any vaccine program. A question arises as to the factors that are associated with the decision to get vaccinated. The present study explored associations between vaccine hesitancy, and the Health Belief Model (HBM) in student samples from the Czech Republic, Israel, and the United States. From August, 2021 through December, 2021, an online survey was distributed in Czech, Hebrew, and English. A total of 447 participants completed the survey. A binomial logistic regression was conducted to ascertain the influence of perceived susceptibility, severity, benefits, and barriers on the likelihood that participants are vaccinated. Results revealed that the factors of perceived severity and perceived benefits explained 52.6% (Nagelkerke R2) of the variance in vaccination. An analysis of Variance (ANOVA) found significant differences between countries for the 4 HBM factors. Based on these findings, it is recommended that policymakers put forth added emphasis on the severity of a virus and the benefits associated with the vaccine. Further, since there appears to be variability between countries in perceptions of the virus, and associated vaccine, governments should consider factors within their own environment when developing a strategy to combat a pandemic. More specifically, government could explore their own strengths, weaknesses, opportunities, and threats as they develop a pandemic strategy. Additional practical and theoretical implications are discussed.

Psychophysiological Predictors of Soldier Performance in Tunnel Warfare: A Field Study on the Correlates of Optimal Performance in a Simulation of Subterranean Combat.

Tunnel operations produce unique psychophysiological activation that is correlated with cognitive impairment and lower performance. This study introduces a new concept: subterranean operational potential (SOP) and assesses its psychophysiological correlates for performance prediction in underground spaces. 138 soldiers of elite infantry battalions, with/without previous experience, who participated in a simulation of tunnel warfare. Physical, psychological, cognitive style, and performance measures were collected. SOP has three sub-components: performance, leadership, and orientation. Leadership and performance both were negatively correlated with perceived stress. Claustrophobia was negatively correlated with leadership. The cognitive style was positively correlated with performance. Saliva cortisol levels were significantly higher before the simulation. Inexperienced and experienced differed in the change in before-after saliva cortisol levels.

Momentary Depressive Feeling Detection Using X (Formerly Twitter) Data: Contextual Language Approach.

BACKGROUND: Depression and momentary depressive feelings are major public health concerns imposing a substantial burden on both individuals and society. Early detection of momentary depressive feelings is highly beneficial in reducing this burden and improving the quality of life for affected individuals. To this end, the abundance of data exemplified by X (formerly Twitter) presents an invaluable resource for discerning insights into individuals' mental states and enabling timely detection of these transitory depressive feelings. OBJECTIVE: The objective of this study was to automate the detection of momentary depressive feelings in posts using contextual language approaches. METHODS: First, we identified terms expressing momentary depressive feelings and depression, scaled their relevance to depression, and constructed a lexicon. Then, we scraped posts using this lexicon and labeled them manually. Finally, we assessed the performance of the Bidirectional Encoder Representations From Transformers (BERT), A Lite BERT (ALBERT), Robustly Optimized BERT Approach (RoBERTa), Distilled BERT (DistilBERT), convolutional neural network (CNN), bidirectional long short-term memory (BiLSTM), and machine learning (ML) algorithms in detecting momentary depressive feelings in posts. RESULTS: This study demonstrates a notable distinction in performance between binary classification, aimed at identifying posts conveying depressive sentiments and multilabel classification, designed to categorize such posts across multiple emotional nuances. Specifically, binary classification emerges as the more adept approach in this context, outperforming multilabel classification. This outcome stems from several critical factors that underscore the nuanced nature of depressive expressions within social media. Our results show that when using binary classification, BERT and DistilBERT (pretrained transfer learning algorithms) may outperform traditional ML algorithms. Particularly, DistilBERT achieved the best performance in terms of area under the curve (96.71%), accuracy (97.4%), sensitivity (97.57%), specificity (97.22%), precision (97.30%), and F1-score (97.44%). DistilBERT obtained an area under the curve nearly 12% points higher than that of the best-performing traditional ML algorithm, convolutional neural network. This study showed that transfer learning algorithms are highly effective in extracting knowledge from posts, detecting momentary depressive feelings, and highlighting their superiority in contextual analysis. CONCLUSIONS: Our findings suggest that contextual language approaches-particularly those rooted in transfer learning-are reliable approaches to automate the early detection of momentary depressive feelings and can be used to develop social media monitoring tools for identifying individuals who may be at risk of depression. The implications are far-reaching because these approaches stand poised to inform the creation of social media monitoring tools and are pivotal for identifying individuals susceptible to depression. By intervening proactively, these tools possess the potential to slow the progression of depressive feelings, effectively mitigating the societal load of depression and fostering improved mental health. In addition to highlighting the capabilities of automated sentiment analysis, this study illuminates its pivotal role in advancing global public health.

Examining the Twitter Discourse on Dementia During Alzheimer's Awareness Month in Canada: Infodemiology Study.

BACKGROUND: Twitter has become a primary platform for public health campaigns, ranging from mental health awareness week to diabetes awareness month. However, there is a paucity of knowledge about how Twitter is being used during health campaigns, especially for Alzheimer's Awareness Month. OBJECTIVE: The purpose of our study was to examine dementia discourse during Canada's Alzheimer's Awareness Month in January to inform future awareness campaigns. METHODS: We collected 1289 relevant tweets using the Twint application in Python from January 1 to January 31, 2022. Thematic analysis was used to analyze the data. RESULTS: Guided by our analysis, 4 primary themes were identified: dementia education and advocacy, fundraising and promotion, experiences of dementia, and opportunities for future actions. CONCLUSIONS: Although our study identified many educational, promotional, and fundraising tweets to support dementia awareness, we also found numerous tweets with cursory messaging (ie, simply referencing January as Alzheimer's Awareness Month in Canada). While these tweets promoted general awareness, they also highlight an opportunity for targeted educational content to counter stigmatizing messages and misinformation about dementia. In addition, awareness strategies partnering with diverse stakeholders (such as celebrities, social media influencers, and people living with dementia and their care partners) may play a pivotal role in fostering dementia dialogue and education. Further research is needed to develop, implement, and evaluate dementia awareness strategies on Twitter. Increased knowledge, partnerships, and research are essential to enhancing dementia awareness during Canada's Alzheimer's Awareness Month and beyond.

Examining the Impact of COVID-19 on People With Dementia From the Perspective of Family and Friends: Thematic Analysis of Tweets.

BACKGROUND: The COVID-19 pandemic is taking a serious toll on people with dementia. Given the rapidly evolving COVID-19 context, policymakers and practitioners require timely, evidence-informed research to address the changing needs and challenges of people with dementia and their family care partners. OBJECTIVE: Using Twitter data, the objective of this study was to examine the COVID-19 impact on people with dementia from the perspective of their family members and friends. METHODS: Using the Twint application in Python, we collected 6243 relevant tweets over a 15-month time frame. The tweets were divided among 11 coders and analyzed using a 6-step thematic analysis process. RESULTS: Based on our analysis, 3 main themes were identified: (1) frustration and structural inequities (eg, denied dignity and inadequate supports), (2) despair due to loss (eg, isolation, decline, and death), and (3) resiliency, survival, and hope for the future. CONCLUSIONS: As the COVID-19 pandemic persists and new variants emerge, people with dementia and their family care partners are facing complex challenges that require timely interventions. More specifically, tackling COVID-19 challenges requires revisiting pandemic policies and protocols to ensure equitable access to health and support services, recognizing the essential role of family care partners, and providing financial assistance and resources to help support people with dementia in the pandemic. Revaluating COVID-19 policies is critical to mitigating the pandemic's impact on people with dementia and their family care partners.

Feigned ADHD Associated Cognitive Impairment: Utility of Integrating an Eye-tracker and the MOXO-dCPT.

OBJECTIVE: The current study assessed the utility of eye-movements measures, gathered while participants performed a commercially available Continuous Performance Test (CPT), to detect feigned ADHD-associated cognitive impairment. METHOD: Healthy simulators (n = 37), ADHD patients (n = 33), and healthy controls (n = 36) performed an eye-tracker integrated MOXO-dCPT and a stand-alone validity indicator. RESULTS: Simulators gazed significantly longer at regions that were irrelevant for successful MOXO-dCPT performance compared to ADHD patients and healthy controls. This eye-movement measure, however, had lower sensitivity than traditional MOXO-dCPT indices. DISCUSSION: Gaze direction measures, gathered while performing a CPT, show initial promise as validity indicators. Traditional CPT measures, however, are more sensitive and therefore offer a more promising path for the establishment of CPT-based validity indicators. The current study is an initial exploration of the issue and further evaluation of both theoretical and practical aspects is mandated.

Detection of Feigned ADHD Using the MOXO-d-CPT.

Objective: The objective of this study was to assess the MOXO-d-CPT utility in detecting feigned ADHD and establish cutoffs with adequate specificity and sensitivity. Method: The study had two phases. First, using a prospective design, healthy adults who simulated ADHD were compared with healthy controls and ADHD patients who performed the tasks to the best of their ability (n = 47 per group). Participants performed the MOXO-d-CPT and an established performance validity test (PVT). Second, the MOXO-d-CPT classification accuracy, employed in Phase 1, was retrospectively compared with archival data of 47 ADHD patients and age-matched healthy controls. Results: Simulators performed significantly worse on all MOXO-d-CPT indices than healthy controls and ADHD patients. Three MOXO-d-CPT indices (attention, hyperactivity, impulsivity) and a scale combining these indices showed adequate discriminative capacity. Conclusion: The MOXO-d-CPT showed promise for the detection of feigned ADHD and, pending replication, can be employed for this aim in clinical practice and ADHD research.