scholarly journals Investigating Young Employee Stressors in Contemporary Society Based on User-Generated Contents

2021 ◽  
Vol 18 (24) ◽  
pp. 13109
Author(s):  
Ning Wang ◽  
Can Wang ◽  
Limin Hou ◽  
Bing Fang
Keyword(s):  
Mental Health ◽  
Language Processing ◽  
Qualitative Content Analysis ◽  
Hybrid Approach ◽  
Learning Technology ◽  
Contemporary Society ◽  
Effective Measure ◽  
Extant Literature ◽  
Novel Approach ◽  
Employee Mental Health

Understanding stressors is an effective measure to decrease employee stress and improve employee mental health. The extant literature mainly focuses on a singular stressor among various aspects of their work or life. In addition, the extant literature generally uses questionnaires or interviews to obtain data. Data obtained in such ways are often subjective and lack authenticity. We propose a novel machine–human hybrid approach to conduct qualitative content analysis of user-generated online content to explore the stressors of young employees in contemporary society. The user-generated online contents were collected from a famous Q&A platform in China and we adopted natural language processing and deep learning technology to discover knowledge. Our results identified three kinds of new stressors, that is, affection from leaders, affection from the social circle, and the gap between dream and reality. These new identified stressors were due to the lack of social security and regulation, frequent occurrences of social media fearmongering, and subjective cognitive bias, respectively. In light of our findings, we offer valuable practical insights and policy recommendations to relieve stress and improve mental health of young employees. The primary contributions of our work are two-fold, as follows. First, we propose a novel approach to explore the stressors of young employees in contemporary society, which is applicable not only in China, but also in other countries and regions. Second, we expand the scope of job demands-resources (JD-R) theory, which is an important framework for the classification of employee stressors.

scholarly journals AI Augmented Approach to Identify Shared Ideas from Large Format Public Consultation

2021 ◽  
Vol 13 (16) ◽  
pp. 9310
Author(s):  
Min-Hsien Weng ◽  
Shaoqun Wu ◽  
Mark Dyer
Keyword(s):  
Language Processing ◽  
Public Consultation ◽  
Learning Technology ◽  
Large Format ◽  
Natural Languages ◽  
Novel Approach ◽  
Public Data ◽  
Wide Range ◽  
Design Values ◽  
Using Data

Public data, contributed by citizens, stakeholders and other potentially affected parties, are becoming increasingly used to collect the shared ideas of a wider community. Having collected large quantities of text data from public consultation, the challenge is often how to interpret the dataset without resorting to lengthy time-consuming manual analysis. One approach gaining ground is the use of Natural Language Processing (NLP) technologies. Based on machine learning technology applied to analysis of human natural languages, NLP provides the opportunity to automate data analysis for large volumes of texts at a scale that would be virtually impossible to analyse manually. Using NLP toolkits, this paper presents a novel approach for identifying and visualising shared ideas from large format public consultation. The approach analyses grammatical structures of public texts to discover shared ideas from sentences comprising subject + verb + object and verb + object that express public options. In particular, the shared ideas are identified by extracting noun, verb, adjective phrases and clauses from subjects and objects, which are then categorised by urban infrastructure categories and terms. The results are visualised in a hierarchy chart and a word tree using cascade and tree views. The approach is illustrated using data collected from a public consultation exercise called “Share an Idea” undertaken in Christchurch, New Zealand, after the 2011 earthquake. The approach has the potential to upscale public participation to identify shared design values and associated qualities for a wide range of public initiatives including urban planning.

Relationship between workplace spirituality, organizational justice and mental health: mediation role of employee engagement

2020 ◽  
Vol 17 (5) ◽  
pp. 627-650 ◽  
Author(s):  
Praveen Kumar Sharma ◽  
Rajeev Kumra
Keyword(s):  
Mental Health ◽  
Organizational Justice ◽  
Employee Engagement ◽  
Meaningful Work ◽  
Workplace Spirituality ◽  
Content Type ◽  
Extant Literature ◽  
Employee Mental Health ◽  
The Relationship

PurposeWorkplace spirituality is presently a prominent research topic and is gaining recognition and importance among industry professionals and academicians. Workplace spirituality is defined as a sense of community, meaningful work and organizational values. The purpose of this research paper is to investigate the relationship between workplace spirituality and mental health, wherein employee engagement is considered as a mediator. Furthermore, this study examines the mediating role of employee engagement in the relationship between organizational justice and mental health.Design/methodology/approachData were gathered from 344 information technology professionals working in India. Structural equation modelling was used to evaluate the model fit of workplace spirituality and its relationship to employee engagement, organizational justice and mental health.FindingsThe results revealed that workplace spirituality and organizational justice significantly and positively predict employee engagement, which is significantly related to employee mental health. The results also revealed that employee engagement significantly partially mediates the relationship between workplace spirituality and mental health as well as the relationship between organizational justice and mental health.Research limitations/implicationsResults of research guide HR professionals, employee mental health concerns can be addressed by promoting workplace spirituality, improving employee engagement strategies and implementing organizational justice policies that are perceived to be fair. This study makes a significant contribution to the extant literature regarding mental health issues in the IT sector.Originality/valueFindings of this research contribute to the area of human resource management and employee engagement. The current study fills a gap in the extant literature by investigating employee engagement intervening mechanism between organizational justice, workplace spirituality and mental health.

Employee mental health treatment seeking: Perceptions of responsibility and resilience

2013 ◽  
Author(s):  
Skye K. Gillispie ◽  
Thomas W. Britt ◽  
Crystal M. Burnette ◽  
Anna C. McFadden ◽  
Chad R. Breeden
Keyword(s):  
Mental Health ◽  
Mental Health Treatment ◽  
Treatment Seeking ◽  
Health Treatment ◽  
Employee Mental Health

scholarly journals Disenchantment, Buffering, and Spiritual Reductionism: A Pedagogy of Secularism for Counseling and Psychotherapy

Religions ◽  
2021 ◽  
Vol 12 (8) ◽  
pp. 612
Author(s):  
Waleed Y. Sami ◽  
John Mitchell Waters ◽  
Amelia Liadis ◽  
Aliza Lambert ◽  
Abigail H. Conley
Keyword(s):  
Mental Health ◽  
Social Work ◽  
Counseling Psychology ◽  
Formal Education ◽  
Contemporary Society ◽  
Religion And Spirituality ◽  
Case Vignettes

The various mental health disciplines (e.g., counseling, psychology, social work) all mandate competence in working with clients from diverse religious and spiritual backgrounds. However, there is growing evidence that practitioners feel ill-equipped to meet the needs of their religiously- and spiritually-diverse clients. Furthermore, formal education on religion and spirituality remains optional within coursework. Research on religion and spirituality is also noted for its reductionism to observable outcomes, leaving much of its nuance uncovered. This paper will utilize philosophies of secularism and explore the concepts of disenchantment, buffering, and coercion, to help illuminate why our contemporary society and our disciplines struggle with this incongruence between stated values and implementation. Case vignettes and recommendations will be provided to help practitioners and educators.

scholarly journals Automated Confirmation of Protein Annotation Using NLP and the UniProtKB Database

2020 ◽  
Vol 11 (1) ◽  
pp. 24
Author(s):  
Jin Tao ◽  
Kelly Brayton ◽  
Shira Broschat
Keyword(s):  
Language Processing ◽  
Fine Tuning ◽  
Support Vector ◽  
Protein Annotation ◽  
Computing Power ◽  
Journal Publication ◽  
Novel Approach ◽  
Uniprotkb Database ◽  
Public Repositories ◽  
Annotation Errors

Advances in genome sequencing technology and computing power have brought about the explosive growth of sequenced genomes in public repositories with a concomitant increase in annotation errors. Many protein sequences are annotated using computational analysis rather than experimental verification, leading to inaccuracies in annotation. Confirmation of existing protein annotations is urgently needed before misannotation becomes even more prevalent due to error propagation. In this work we present a novel approach for automatically confirming the existence of manually curated information with experimental evidence of protein annotation. Our ensemble learning method uses a combination of recurrent convolutional neural network, logistic regression, and support vector machine models. Natural language processing in the form of word embeddings is used with journal publication titles retrieved from the UniProtKB database. Importantly, we use recall as our most significant metric to ensure the maximum number of verifications possible; results are reported to a human curator for confirmation. Our ensemble model achieves 91.25% recall, 71.26% accuracy, 65.19% precision, and an F1 score of 76.05% and outperforms the Bidirectional Encoder Representations from Transformers for Biomedical Text Mining (BioBERT) model with fine-tuning using the same data.

scholarly journals Using NLP in openEHR archetypes retrieval to promote interoperability: a feasibility study in China

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Bo Sun ◽  
Fei Zhang ◽  
Jing Li ◽  
Yicheng Yang ◽  
Xiaolin Diao ◽  
...  
Keyword(s):  
Information Sharing ◽  
Language Processing ◽  
Medical Information ◽  
Medical Professional ◽  
Semantic Interoperability ◽  
Medical Information System ◽  
Search Term ◽  
Search Terms ◽  
Novel Approach ◽  
Test Sets

Abstract Background With the development and application of medical information system, semantic interoperability is essential for accurate and advanced health-related computing and electronic health record (EHR) information sharing. The openEHR approach can improve semantic interoperability. One key improvement of openEHR is that it allows for the use of existing archetypes. The crucial problem is how to improve the precision and resolve ambiguity in the archetype retrieval. Method Based on the query expansion technology and Word2Vec model in Nature Language Processing (NLP), we propose to find synonyms as substitutes for original search terms in archetype retrieval. Test sets in different medical professional level are used to verify the feasibility. Result Applying the approach to each original search term (n = 120) in test sets, a total of 69,348 substitutes were constructed. Precision at 5 (P@5) was improved by 0.767, on average. For the best result, the P@5 was up to 0.975. Conclusions We introduce a novel approach that using NLP technology and corpus to find synonyms as substitutes for original search terms. Compared to simply mapping the element contained in openEHR to an external dictionary, this approach could greatly improve precision and resolve ambiguity in retrieval tasks. This is helpful to promote the application of openEHR and advance EHR information sharing.

scholarly journals HPM: A Hybrid Model for User’s Behavior Prediction Based on N-Gram Parsing and Access Logs

2020 ◽  
Vol 2020 ◽  
pp. 1-18
Author(s):  
Sonia Setia ◽  
Verma Jyoti ◽  
Neelam Duhan
Keyword(s):  
Web Mining ◽  
Contextual Information ◽  
Hybrid Approach ◽  
Web Pages ◽  
Continuous Growth ◽  
Novel Approach ◽  
Content Mining ◽  
Long Access ◽  
N Gram ◽  
The Individual

The continuous growth of the World Wide Web has led to the problem of long access delays. To reduce this delay, prefetching techniques have been used to predict the users’ browsing behavior to fetch the web pages before the user explicitly demands that web page. To make near accurate predictions for users’ search behavior is a complex task faced by researchers for many years. For this, various web mining techniques have been used. However, it is observed that either of the methods has its own set of drawbacks. In this paper, a novel approach has been proposed to make a hybrid prediction model that integrates usage mining and content mining techniques to tackle the individual challenges of both these approaches. The proposed method uses N-gram parsing along with the click count of the queries to capture more contextual information as an effort to improve the prediction of web pages. Evaluation of the proposed hybrid approach has been done by using AOL search logs, which shows a 26% increase in precision of prediction and a 10% increase in hit ratio on average as compared to other mining techniques.

Leveraging Twitter Using Artificial Intelligence to Explore Mental Health Insights in the UK During the COVID-19 Pandemic (Preprint)

2021 ◽  
Author(s):  
Christopher Marshall ◽  
Kate Lanyi ◽  
Rhiannon Green ◽  
Georgie Wilkins ◽  
Fiona Pearson ◽  
...  
Keyword(s):  
Mental Health ◽  
Artificial Intelligence ◽  
Language Processing ◽  
Mental Health Problems ◽  
Well Being ◽  
Research Activity ◽  
Health Crisis ◽  
Key Topics ◽  
The Uk ◽  
The Impact

BACKGROUND There is increasing need to explore the value of soft-intelligence, leveraged using the latest artificial intelligence (AI) and natural language processing (NLP) techniques, as a source of analysed evidence to support public health research activity and decision-making. OBJECTIVE The aim of this study was to further explore the value of soft-intelligence analysed using AI through a case study, which examined a large collection of UK tweets relating to mental health during the COVID-19 pandemic. METHODS A search strategy comprising a list of terms related to mental health, COVID-19, and lockdown restrictions was developed to prospectively collate relevant tweets via Twitter’s advanced search application programming interface over a 24-week period. We deployed a specialist NLP platform to explore tweet frequency and sentiment across the UK and identify key topics of discussion. A series of keyword filters were used to clean the initial data retrieved and also set up to track specific mental health problems. Qualitative document analysis was carried out to further explore and expand upon the results generated by the NLP platform. All collated tweets were anonymised RESULTS We identified and analysed 286,902 tweets posted from UK user accounts from 23 July 2020 to 6 January 2021. The average sentiment score was 50%, suggesting overall neutral sentiment across all tweets over the study period. Major fluctuations in volume and sentiment appeared to coincide with key changes to any local and/or national social-distancing measures. Tweets around mental health were polarising, discussed with both positive and negative sentiment. Key topics of consistent discussion over the study period included the impact of the pandemic on people’s mental health (both positively and negatively), fear and anxiety over lockdowns, and anger and mistrust toward the government. CONCLUSIONS Through the primary use of an AI-based NLP platform, we were able to rapidly mine and analyse emerging health-related insights from UK tweets into how the pandemic may be impacting people’s mental health and well-being. This type of real-time analysed evidence could act as a useful intelligence source that agencies, local leaders, and health care decision makers can potentially draw from, particularly during a health crisis.

Cultural competence in multi-family psychoeducation groups: The experiences of Russian-speaking immigrant mothers of adults with severe mental illness

2021 ◽  
pp. 136346152110583
Author(s):  
Evgeny Knaifel
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Cultural Competence ◽  
Severe Mental Illness ◽  
Former Soviet Union ◽  
Health Knowledge ◽  
Qualitative Content Analysis ◽  
Family Psychoeducation ◽  
Family Coping ◽  
Immigrant Mothers

The successful integration of cultural competence with evidence-based practices in mental health services is still limited for particular cultural populations. The current study explored culturally adapted family psychoeducation intervention for immigrants from the former Soviet Union (FSU) in Israel who care for a family member with severe mental illness (SMI). Semi-structured in-depth interviews were conducted with 18 immigrant mothers about their experience of taking part in Russian-speaking multi-family psychoeducation groups (MFPGs). Qualitative content analysis revealed five salient processes and changes that participants attributed to their engagement in the intervention: 1) from a language barrier to utilization of and satisfaction with services; 2) from a lack of information to acquiring new mental health knowledge; 3) from harboring a family secret to exposure and sharing; 4) from social isolation to cultural belonging and support; 5) from families blurring boundaries to physical and emotional separation. The results showed that these changes—linguistic, cognitive, emotional, socio-cultural and relational—improved family coping and recovery. Implications for cultural adaptation of family psychoeducation for Russian-speaking immigrants are discussed.

Using Natural Language Processing to Assess the Psychological Effect of COVID-19 Pandemic on Insomnia via Tweets: A Pre-Post Retrospective Pilot Study (Preprint)

2021 ◽  
Author(s):  
Arash Maghsoudi ◽  
Sara Nowakowski ◽  
Ritwick Agrawal ◽  
Amir Sharafkhaneh ◽  
Sadaf Aram ◽  
...  
Keyword(s):  
Mental Health ◽  
Social Media ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Temporal Analysis ◽  
Health Condition ◽  
Additional Stress ◽  
Mental Health Condition ◽  
Twitter Users

BACKGROUND The COVID-19 pandemic has imposed additional stress on population health that may result in a higher incidence of insomnia. In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. OBJECTIVE In this study, we hypothesized that using natural language processing (NLP) to explore social media would help to identify the mental health condition of the population experiencing insomnia after the outbreak of COVID-19. METHODS We designed a pre-post retrospective study using public social media content from Twitter. We categorized tweets based on time into two intervals: prepandemic (01/01/2019 to 01/01/2020) and pandemic (01/01/2020 to 01/01/2021). We used NLP to analyze polarity (positive/negative) and intensity of emotions and also users’ tweets psychological states in terms of sadness, anxiety and anger by counting the words related to these categories in each tweet. Additionally, we performed temporal analysis to examine the effect of time on the users’ insomnia experience. RESULTS We extracted 268,803 tweets containing the word insomnia (prepandemic, 123,293 and pandemic, 145,510). The odds of negative tweets (OR, 1.31; 95% CI, 1.29-1.33), anger (OR, 1.19; 95% CI, 1.16-1.21), and anxiety (OR, 1.24; 95% CI: 1.21-1.26) were higher during the pandemic compared to prepandemic. The likelihood of negative tweets after midnight was higher than for other daily intevals, comprising approximately 60% of all negative insomnia-related tweets in 2020 and 2021 collectively. CONCLUSIONS Twitter users shared more negative tweets about insomnia during the pandemic than during the year before. Also, more anger and anxiety-related content were disseminated during the pandemic on the social media platform. Future studies using an NLP framework could assess tweets about other psychological distress, habit changes, weight gain due to inactivity, and the effect of viral infection on sleep.

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