Sentiment and emotion trends in nurses' tweets about the COVID-19 pandemic

In Pursuit of the Practice Ready Nurse: Insights From a National Survey

OBJECTIVE

To understand how practice readiness for nursing is currently perceived, a survey was conducted among professional nurses spanning practice and academic settings.

BACKGROUND

The concept of practice readiness is ambiguous because of variations in interpretation and expectations between educators, employers, and new nurses. It is challenging to improve the preparation and onboarding of new nurses without this understanding.

METHOD

A cross-sectional survey was electronically completed by 437 nurses. Survey data were analyzed using thematic analysis. A subanalysis was performed to examine differences by demographics.

RESULTS

The data were categorized into 6 themes: critical thinking and analytical skills, professionalism and ethical practice, technical and clinical proficiency, compassion and patient-centered care, interpersonal skills and collaboration, and adaptability and continuous learning. These findings are a 1st step in understanding how nurses currently describe practice readiness.

The Utilization of Natural Language Processing for Analyzing Social Media Data in Nursing Research: A Scoping Review

Aim: This scoping review aimed to identify and synthesize the evidence in existing nursing studies that used natural language processing to analyze social media data, and the relevant procedures, techniques, tools, and ethical issues. Background: Social media has widely integrated into both everyday life and the nursing profession, resulting in the accumulation of extensive nursing-related social media data. The analysis of such data facilitates the generation of evidence thereby aiding in the formation of better policies. Natural language processing has emerged as a promising methodology for analyzing social media data in the field of nursing. However, the extent of natural language processing applications in analyzing nursing-related social media data remains unknown. Evaluation: A scoping review was conducted. PubMed, CINAHL, Web of Science and IEEE Xplore were searched. Studies were screened based on inclusion criteria. Relevant data were extracted and summarized using a descriptive approach. Key Issues: In total, 38 studies were included for the final analysis. Topic modeling and sentiment analysis were the most frequently employed natural language processing techniques. The most used topic modeling algorithm was latent Dirichlet allocation. The dictionary-based approach was the most utilized sentiment analysis approach, and the National Research Council Sentiment and Emotion Lexicons was the most used sentiment dictionary. Natural language processing tools such as Python (NLTK, Jieba, spaCy, and KoNLP library) and R (LDAvis, Jaccard, ldatuning, and SentiWordNet packages) were documented. A significant proportion of the included studies did not obtain ethical approval and did not conduct data anonymization on social media users' information. Conclusion: This scoping review summarized the extent of natural language processing techniques adoption in nursing and relevant procedures and tools, offering valuable resources for researchers who are interested in discovering knowledge from social media data. The study also highlighted that the application of natural language processing for analyzing nursing-related social media data is still emerging, indicating opportunities for future methodological improvements. Implications for Nursing Management: There is a need for a standardized management framework for conducting and reporting studies using natural language processing techniques in the analysis of nursing-related social media data. The findings could inform the development of regulatory policies by nursing authorities.

Characteristics of sleep disturbance across two waves of the COVID-19 pandemic among nursing staffs

Objectives

COVID-19 has impacted human lifestyles, including sleep quality. For nursing staff, sleep disorders not only impact their health and daily lives but also have implications for patient safety. The objective of this study is to explore the impact of the COVID-19 pandemic on the psychological and social aspects of nursing staff and the factors influencing their sleep quality through a two-wave survey.

Methods

Nursing staff from a psychiatric hospital in southern Taiwan were recruited in two waves during the COVID-19 epidemic. The level of sleep disturbance and related variables, such as Lo's Healthy and Happy Lifestyle Scale (LHHLS) and Societal Influences Survey Questionnaire (SISQ), were collected through self-report questionnaires. Factors related to the level of sleep disturbance were examined using univariate linear regression and multilevel linear regression.

Results

508 nursing staff members were included in the study, with 254 members in each wave. A significant difference was found between the two waves in the positive thinking of LHHLS and all subscales of SISQ. During the second wave, sleep disturbances were mainly related to self-efficacy, positive thinking, social anxiety, and social desirability. At the fourth wave, sleep disturbances were mainly related to self-efficacy, positive thinking, and social anxiety. However, these effects change when the trend of the epidemic shifts, and other factors are taken into account.

Conclusions

This study analyzed the factors related to the sleep quality of nursing staff during the COVID-19 pandemic. We preliminarily explored the impact of the COVID-19 pandemic on the sleep quality of nursing staff. However, determining whether the end of the epidemic has reduced the impact on nursing staff requires further research.

An Enhanced IDBO-CNN-BiLSTM Model for Sentiment Analysis of Natural Disaster Tweets

The Internet's development has prompted social media to become an essential channel for disseminating disaster-related information. Increasing the accuracy of emotional polarity recognition in tweets is conducive to the government or rescue organizations understanding the public's demands and responding appropriately. Existing sentiment analysis models have some limitations of applicability. Therefore, this research proposes an IDBO-CNN-BiLSTM model combining the swarm intelligence optimization algorithm and deep learning methods. First, the Dung Beetle Optimization (DBO) algorithm is improved by adopting the Latin hypercube sampling, integrating the Osprey Optimization Algorithm (OOA), and introducing an adaptive Gaussian-Cauchy mixture mutation disturbance. The improved DBO (IDBO) algorithm is then utilized to optimize the Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-BiLSTM) model's hyperparameters. Finally, the IDBO-CNN-BiLSTM model is constructed to classify the emotional tendencies of tweets associated with the Hurricane Harvey event. The empirical analysis indicates that the proposed model achieves an accuracy of 0.8033, outperforming other single and hybrid models. In contrast with the GWO, WOA, and DBO algorithms, the accuracy is enhanced by 2.89%, 2.82%, and 2.72%, respectively. This study proves that the IDBO-CNN-BiLSTM model can be applied to assist emergency decision-making in natural disasters.

Understanding global research trends in the control and prevention of infectious diseases for children: Insights from text mining and topic modeling

INTRODUCTION

The emergence of novel infectious diseases has amplified the urgent need for effective prevention strategies, especially ones targeting vulnerable populations such as children. Factors such as the high incidence of both emerging and existing infectious diseases, delays in vaccinations, and routine exposure in communal settings heighten children's susceptibility to infections. Despite this pressing need, a comprehensive exploration of research trends in this domain remains lacking. This study aims to address this gap by employing text mining and modeling techniques to conduct a comprehensive analysis of the existing literature, thereby identifying emerging research trends in infectious disease prevention among children.

METHODS

A cross-sectional text mining approach was adopted, focusing on journal articles published between January 1, 2003, and August 31, 2022. These articles, related to infectious disease prevention in children, were sourced from databases such as PubMed, CINAHL, MEDLINE (Ovid), Scopus, and Korean RISS. The data underwent preprocessing using the Natural Language Toolkit (NLTK) in Python, with a semantic network analysis and topic modeling conducted using R software.

RESULTS

The final dataset comprised 509 journal articles extracted from multiple databases. The study began with a word frequency analysis to pinpoint relevant themes, subsequently visualized through a word cloud. Dominant terms encompassed "vaccination," "adolescent," "infant," "parent," "family," "school," "country," "household," "community," "HIV," "HPV," "COVID-19," "influenza," and "diarrhea." The semantic analysis identified "age" as a key term across infection, control, and intervention discussions. Notably, the relationship between "hand" and "handwashing" was prominent, especially in educational contexts linked with "school" and "absence." Latent Dirichlet Allocation (LDA) topic modeling further delineated seven topics related to infectious disease prevention for children, encompassing (1) educational programs, (2) vaccination efforts, (3) family-level responses, (4) care for immunocompromised individuals, (5) country-specific responses, (6) school-based strategies, and (7) persistent threats from established infectious diseases.

CONCLUSION

The study emphasizes the indispensable role of personalized interventions tailored for various child demographics, highlighting the pivotal contributions of both parental guidance and school participation.

CLINICAL RELEVANCE

The study provides insights into the complex public health challenges associated with preventing and managing infectious diseases in children. The insights derived could inform the formulation of evidence-based public health policies, steering practical interventions and fostering interdisciplinary synergy for holistic prevention strategies.

Sentiment Analysis of Patient- and Family-Related Sepsis Events: Exploratory Study

BACKGROUND

Despite the life-threatening nature of sepsis, little is known about the emotional experiences of patients and their families during sepsis events. We conducted a sentiment analysis pertaining to sepsis incidents involving patients and families, leveraging textual data retrieved from a publicly available blog post disseminated by the Centers for Disease Control and Prevention (CDC).

OBJECTIVE

This investigation involved a sentiment analysis of patient- and family-related sepsis events, leveraging text responses sourced from a publicly accessible blog post disseminated by the CDC. Driven by the imperative to elucidate the emotional dynamics encountered by patients and their families throughout sepsis incidents, the overarching aims centered on elucidating the emotional ramifications of sepsis on both patients and their families and discerning potential avenues for enhancing the quality of sepsis care.

METHODS

The research used a cross-sectional data mining methodology to investigate the sentiments and emotional aspects linked to sepsis, using a data set sourced from the CDC, which encompasses 170 responses from both patients and caregivers, spanning the period between September 2014 and September 2020. This investigation used the National Research Council Canada Emotion Lexicon for sentiment analysis, coupled with a combination of manual and automated techniques to extract salient features from textual responses. The study used negative binomial least absolute shrinkage and selection operator regressions to ascertain significant textual features that correlated with specific emotional states. Moreover, the visualization of Plutchik's Wheel of Emotions facilitated the discernment of prevailing emotions within the data set.

RESULTS

The results showed that patients and their families experienced a range of emotions during sepsis events, including fear, anxiety, sadness, and gratitude. Our analyses revealed an estimated incidence rate ratio (IRR) of 1.35 for fear-related words and a 1.51 IRR for sadness-related words when mentioning "hospital" in sepsis-related experiences. Similarly, mentions of "intensive care unit" were associated with an average occurrence of 12.3 fear-related words and 10.8 sadness-related words. Surviving patients' experiences had an estimated 1.15 IRR for joy-related words, contrasting with discussions around organ failure, which were associated with multiple negative emotions including disgust, anger, fear, and sadness. Furthermore, mentions of "death" were linked to more fear and anger words but fewer joy-related words. Conversely, longer timelines in sepsis events were associated with more joy-related words and fewer fear-related words, potentially indicating improved emotional adaptation over time.

CONCLUSIONS

The study's outcomes underscore the imperative for health care providers to integrate emotional support alongside medical interventions for patients and families affected by sepsis, emphasizing the emotional toll incurred and highlighting the necessity of acknowledgment and resolution, advocating for the use of sentiment analysis as a means to tailor personalized emotional aid, and thereby potentially augmenting both patient and family welfare and overall outcomes.

Examination of Nursing Staffs' Perceptions of the COVID-19 Vaccine Using the Health Belief Model

BACKGROUND

The coronavirus (COVID-19) pandemic profoundly impacted patient care across the United States.

OBJECTIVE

To examine nursing staffs' perceptions of the COVID-19 vaccine using the Health Belief Model (HBM) as a theoretical framework.

METHODS

A cross-sectional, anonymous, web-based survey was completed by practicing nursing staffs throughout the United States. Analyses involved descriptive and comparative statistics.

RESULTS

Of the 294 nursing staff who completed surveys, 50% were between 18 and 37 years of age, and 73.1% were registered nurses, with 49.3% employed in a hospital setting. Nursing staff reported their primary reason for vaccination was concern for others (mean: 84.44; SD: 28.26), vaccine prevents spread (mean: 81.85; SD: 28.94), and own health (mean: 79.63; SD: 30.0). Influencing factors that predicted vaccination included age, Wilks' Λ = 0.76, F(32, 919.86) = 2.20, p < .001, η2partial = 0.066, and the vaccine mandate, Wilks' Λ = 0.63, F(8, 249) = 18.61, p < .001, η2partial = 0.374.

CONCLUSION

Nursing staffs' perceptions using the HBM as a theoretical framework provided insight into their decisions to receive the COVID-19 vaccine. Further research is warranted to examine nurses' attitudes and factors that influence their decision-making regarding vaccination.

Quantitative and qualitative analysis of individual experiences post botulinum toxin injection - United Kingdom Survey

Introduction

In the United Kingdom (UK), complications that arise following the administration of Botulinum Toxin are reported to the Medicines and Health Regulatory Agency (MHRA) via the Yellow Card Reporting Scheme. Over the past decade, there has been a significant increase in the number of non-surgical aesthetic procedures. Concerns have been raised that the MHRA is not fully capturing complications in terms of volume and impact on patients.

Aim

This novel study explores the lived experiences of individuals who have experienced an adverse event following administration of Botulinum Toxin for aesthetic purposes. Using a combination of qualitative and quantitative methodologies, this analysis evaluates data relating to long-lasting physical, psychological, emotional, and financial sequelae of complications arising from cosmetic Botulinum Toxin injections in the UK.

Methods

A mixed method, qualitative and quantitative approach was adopted to gain comprehensive insights into patients' experiences. A focus group which comprised patient representatives, psychologists, and researchers reached a consensus on a 17-question survey which was disseminated via social media channels. Deductive thematic analysis was used to analyse coded themes. Furthermore, for secondary analysis, sentiment analysis was used computationally as an innovative approach to identify and categorise free text responses associated with sentiments using natural language processing (NLP).

Results

In the study, 655 responses were received, with 287 (44%) of respondents completing all questions. The mean age of respondents was 42.6 years old. 94.1% of respondents identified as female. In the sample, 79% of respondents reported an adverse event following their procedure, with the most common event being reported as 'anxiety'. Findings revealed that 69% of respondents reported long-lasting adverse effects. From the responses, 68.4% reported not having recovered physically, 63.5% of respondents stated that they had not recovered emotionally from complications, and 61.7% said that they have not recovered psychologically. In addition, 84% of respondents stated that they do not know who regulates the aesthetics industry. Furthermore, 92% of participants reported that their clinic or practitioner did not inform them about the Yellow Card Reporting Scheme. The sentiment analysis using the AFINN Lexicon yielded adjusted scores ranging from -3 to +2, with a mean value of -1.58.

Conclusion

This is the largest survey in the UK completed by patients who experienced an adverse outcome following the aesthetic administration of Botulinum Toxin. Our study highlights the extent of the challenges faced by patients who experience an adverse event from physical, emotional, psychological, and financial perspectives. The lack of awareness of MHRA reporting structures and the lack of regulation within the UK's cosmetic injectables sector represent a significant public health challenge.

A Correlational Study Of Midwives' Self-Compassion, Psychosocial Health, and Well-Being During the First Wave of COVID-19: What Have We Learned?

INTRODUCTION

During the first wave of the COVID-19 pandemic, midwives worked in a threatening environment and worried about themselves and their families becoming infected. Self-compassion is defined as an attitude of self-kindness that is supported by a balanced attitude toward negative thoughts or feelings and may contribute to the psychosocial health and well-being. The purpose of this study was to describe midwives' self-compassion, psychosocial health, and well-being and the correlation between them.

METHODS

This was a descriptive correlational study using a survey administered online during May, 2020. Participants included midwives who worked in labor and delivery units across Israel during the beginning of the COVID-19 pandemic. Measures included a demographic questionnaire; the Self-Compassion Scale Short Form (SCS-SF), which has 12 items in 6 subscales; and the psychosocial health and well-being questionnaire, a short version of the Copenhagen Psychosocial Questionnaire, which has 24 items in 6 subscales.

RESULTS

Participants (N = 144) reported a moderate-high level of self-compassion with a mean (SD) SCS-SF score of 3.57 (0.69). The mean (SD) psychosocial well-being score was 30.72 (13.57). The burnout subscale score had the highest mean (46.27), representing a high level of burnout. A minority (11.3%) of midwives considered resigning their midwifery positions. A higher level of self-compassion correlated with better psychosocial well-being (r = -0.466; P < .001). The highest correlation was found between the SCS-SF and the psychosocial health and well-being subscale for depressive symptoms (r = -0.574; P < .001).

DISCUSSION

During the first wave of the COVID-19 pandemic, midwives had a moderate-high grade of self-compassion and good psychosocial well-being. Midwives with higher self-compassion had better psychosocial well-being. The findings could inform the development of programs to increase midwives' self-compassion, and psychosocial well-being and the quality of midwifery care, in times of stability and during future pandemics or disasters.

Evaluating the Applicability of Existing Lexicon-Based Sentiment Analysis Techniques on Family Medicine Resident Feedback Field Notes: Retrospective Cohort Study

BACKGROUND

Field notes, a form for resident-preceptor clinical encounter feedback, are widely adopted across Canadian medical residency training programs for documenting residents' performance. This process generates a sizeable cumulative collection of feedback text, which is difficult for medical education faculty to navigate. As sentiment analysis is a subfield of text mining that can efficiently synthesize the polarity of a text collection, sentiment analysis may serve as an innovative solution.

OBJECTIVE

This study aimed to examine the feasibility and utility of sentiment analysis using 3 popular sentiment lexicons on medical resident field notes.

METHODS

We used a retrospective cohort design, curating text data from University of Toronto medical resident field notes gathered over 2 years (from July 2019 to June 2021). Lexicon-based sentiment analysis was applied using 3 standardized dictionaries, modified by removing ambiguous words as determined by a medical subject matter expert. Our modified lexicons assigned words from the text data a sentiment score, and we aggregated the word-level scores to a document-level polarity score. Agreement between dictionaries was assessed, and the document-level polarity was correlated with the overall preceptor rating of the clinical encounter under assessment.

RESULTS

Across the 3 original dictionaries, approximately a third of labeled words in our field note corpus were deemed ambiguous and were removed to create modified dictionaries. Across the 3 modified dictionaries, the mean sentiment for the "Strengths" section of the field notes was mildly positive, while it was slightly less positive in the "Areas of Improvement" section. We observed reasonable agreement between dictionaries for sentiment scores in both field note sections. Overall, the proportion of positively labeled documents increased with the overall preceptor rating, and the proportion of negatively labeled documents decreased with the overall preceptor rating.

CONCLUSIONS

Applying sentiment analysis to systematically analyze field notes is feasible. However, the applicability of existing lexicons is limited in the medical setting, even after the removal of ambiguous words. Limited applicability warrants the need to generate new dictionaries specific to the medical education context. Additionally, aspect-based sentiment analysis may be applied to navigate the more nuanced structure of texts when identifying sentiments. Ultimately, this will allow for more robust inferences to discover opportunities for improving resident teaching curriculums.

The emotional side of taking part in a cancer clinical trial

BACKGROUND

Taking part in a cancer clinical trial often represents a source of psychological distress and emotional activation among patients and their caregivers. Nowadays, social media platforms provide a space for these groups to freely express and share their emotional experiences.

AIMS

We aimed to reveal the most prevalent basic and complex emotions and sentiments in the posts of the patients and caregivers contemplating clinical trials on Reddit. Additionally, we aimed to categorize the types of users and posts.

METHODS

With the use of keywords referring to clinical trials, we searched for public posts on the subreddit 'cancer'. R studio v. 4.1.2 (2021-11-01) and NRC Emotion Lexicon was used for analysis. Following the theoretical framework of Plutchik's wheel of emotions, the analysis included: 8 basic emotions (anger, fear, anticipation, trust, surprise, sadness, joy, and disgust) and 4 types of complex emotions (primary, secondary, tertiary, and opposite dyads). We utilized the package 'PyPlutchik' to visualize the emotion wheels in Python 3.10.5.

RESULTS

A total of 241 posts were included in the final database. User types (129 patients, 112 caregivers) and post types (142 expressed shared experience, 77 expressed advice, and 85 conveyed both) were identified. Both positive (N = 2557, M = .68) and negative (N = 2154, M = .57) sentiments were high. The most prevalent basic emotions were: fear (N = 1702, M = .45), sadness (N = 1494, M = .40), trust (N = 1470, M = .44), and anticipation (N = 1376, M = .37). The prevalence of complex/dyadic emotions and their interpretation is further discussed.

CONCLUSION

In this contribution, we identified and discussed prevalent emotions such as fear, sadness, optimism, hope, despair, and outrage that mirror the psychological state of users and affect the medical choices they make. The insights gained in our study contribute to the understanding of the barriers and reinforcers to participation in trials and can improve the ability of healthcare professionals to assist patients when confronted with this choice.

Changes in Public Sentiment under the Background of Major Emergencies-Taking the Shanghai Epidemic as an Example

The occurrence of major health events can have a significant impact on public mood and mental health. In this study, we selected Shanghai during the 2019 novel coronavirus pandemic as a case study and Weibo texts as the data source. The ERNIE pre-training model was used to classify the text data into five emotional categories: gratitude, confidence, sadness, anger, and no emotion. The changes in public sentiment and potential influencing factors were analyzed with the emotional sequence diagram method. We also examined the causal relationship between the epidemic and public sentiment, as well as positive and negative emotions. The study found: (1) public sentiment during the epidemic was primarily affected by public behavior, government behavior, and the severity of the epidemic. (2) From the perspective of time series changes, the changes in public emotions during the epidemic were divided into emotional fermentation, emotional climax, and emotional chaos periods. (3) There was a clear causal relationship between the epidemic and the changes in public emotions, and the impact on negative emotions was greater than that of positive emotions. Additionally, positive emotions had a certain inhibitory effect on negative emotions.