scholarly journals Insights into the impact on daily life of the COVID-19 pandemic and effective coping strategies from free-text analysis of people's collective experiences

2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Adam Hampshire ◽  
Peter J. Hellyer ◽  
William Trender ◽  
Samuel R. Chamberlain
Keyword(s):  
Coping Strategies ◽  
Language Processing ◽  
Text Analysis ◽  
Lived Experiences ◽  
Latent Dirichlet Allocation ◽  
Negative Impact ◽  
Free Text ◽  
The United Kingdom ◽  
Digital Interventions ◽  
The Impact

There has been considerable speculation regarding how people cope during the COVID-19 pandemic; however, surveys requiring selection from prespecified answers are limited by researcher views and may overlook the most effective measures. Here, we apply an unbiased approach that learns from people's collective lived experiences through the application of natural-language processing of their free-text reports. At the peak of the first lockdown in the United Kingdom, 51 113 individuals provided free-text responses regarding self-perceived positive and negative impact of the pandemic, as well as the practical measures they had found helpful during this period. Latent Dirichlet Allocation identified, in an unconstrained data-driven manner, the most common impact and advice topics. We report that six negative topics and seven positive topics are optimal for capturing the different ways people reported being affected by the pandemic. Forty-five topics were required to optimally summarize the practical coping strategies that they recommended. General linear modelling showed that the prevalence of these topics covaried substantially with age. We propose that a wealth of coping measures may be distilled from the lived experiences of the general population. These may inform feasible individually tailored digital interventions that have relevance during and beyond the pandemic.

The impact of the COVID-19 pandemic on autistic adults – a survey

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Conor James Davidson ◽  
Keri Lodge ◽  
Alwyn Kam
Keyword(s):  
Coping Strategies ◽  
Design Methodology ◽  
Online Survey ◽  
Negative Impact ◽  
Significant Deterioration ◽  
Cross Sectional ◽  
Content Type ◽  
Autistic People ◽  
Autistic Adults ◽  
The Impact

Purpose To date there has been limited research on the impact of the COVID-19 pandemic on autistic people. This study aims to present the results of a survey of autistic people (n = 51) conducted by a UK specialist autism team. Design/methodology/approach A cross-sectional online survey. Findings A total of 72% respondents reported either some or significant deterioration in mental health during the pandemic. The issues that caused most negative impact were uncertainty over what will happen next and disruption of normal routine. Respondents reported a variety of coping strategies to help them through the pandemic. Originality/value To date there has been little research looking specifically at the impact of the COVID-19 pandemic on autistic people. This paper adds weight to the evidence that the pandemic has had a particularly severe impact on autistic adults and includes useful information on potential coping strategies for this population.

“Women Are Cancer, You Shouldn’t Be Working in Sport”: Sport Psychologists’ Lived Experiences of Sexism in Sport

2020 ◽  
pp. 1-12
Author(s):  
Aura Goldman ◽  
Misia Gervis
Keyword(s):  
United Kingdom ◽  
Focus Groups ◽  
Sport Psychology ◽  
Thematic Analysis ◽  
Lived Experiences ◽  
The United Kingdom ◽  
Power Differentials ◽  
The Feminine ◽  
Gendered Power ◽  
The Impact

Though sexism has been recognized as problematic in sport, its impact on female sport psychologists in the United Kingdom has not yet been investigated. The purpose of this research was to explore the impact of sexism and its influence on practice. Four semistructured focus groups were conducted, comprising 11 sport psychologists who worked in the United Kingdom. Thematic analysis revealed four general themes: the environment, privileging masculinity, acts of sexism, and the feminine. Participants’ discourse suggests that female sport psychologists are impacted by sexism in their workplaces. Gendered power differentials, coupled with the low status of sport psychology within sport, exacerbated the challenges faced by female sport psychologists. This study contributes to making up for the dearth of research on the impact of sexism on sport psychologists. Suggestions are made with regard to implications for practice.

scholarly journals Identifying Medication-related Intents from a Bidirectional Text Messaging Platform for Hypertension Management: An Unsupervised Learning Approach

2021 ◽  
Author(s):  
Anahita Davoudi ◽  
Natalie Lee ◽  
Thaibinh Luong ◽  
Timothy Delaney ◽  
Elizabeth Asch ◽  
...  
Keyword(s):  
Blood Pressure ◽  
Unsupervised Learning ◽  
Language Processing ◽  
Text Messaging ◽  
Latent Dirichlet Allocation ◽  
Clinical Care ◽  
Hypertension Management ◽  
Free Text ◽  
Significant Heterogeneity ◽  
Text Data

Background: Free-text communication between patients and providers is playing an increasing role in chronic disease management, through platforms varying from traditional healthcare portals to more novel mobile messaging applications. These text data are rich resources for clinical and research purposes, but their sheer volume render them difficult to manage. Even automated approaches such as natural language processing require labor-intensive manual classification for developing training datasets, which is a rate-limiting step. Automated approaches to organizing free-text data are necessary to facilitate the use of free-text communication for clinical care and research. Objective: We applied unsupervised learning approaches to 1) understand the types of topics discussed and 2) to learn medication-related intents from messages sent between patients and providers through a bi-directional text messaging system for managing participant blood pressure. Methods: This study was a secondary analysis of de-identified messages from a remote mobile text-based employee hypertension management program at an academic institution. In experiment 1, we trained a Latent Dirichlet Allocation (LDA) model for each message type (inbound-patient and outbound-provider) and identified the distribution of major topics and significant topics (probability >0.20) across message types. In experiment 2, we annotated all medication-related messages with a single medication intent. Then, we trained a second LDA model (medLDA) to assess how well the unsupervised method could identify more fine-grained medication intents. We encoded each medication message with n-grams (n-1-3 words) using spaCy, clinical named entities using STANZA, and medication categories using MedEx, and then applied Chi-square feature selection to learn the most informative features associated with each medication intent. Results: A total of 253 participants and 5 providers engaged in the program generating 12,131 total messages: 47% patient messages and 53% provider messages. Most patient messages correspond to blood pressure (BP) reporting, BP encouragement, and appointment scheduling. In contrast, most provider messages correspond to BP reporting, medication adherence, and confirmatory statements. In experiment 1, for both patient and provider messages, most messages contained 1 topic and few with more than 3 topics identified using LDA. However, manual review of some messages within topics revealed significant heterogeneity even within single-topic messages as identified by LDA. In experiment 2, among the 534 medication messages annotated with a single medication intent, most of the 282 patient medication messages referred to medication request (48%; n=134) and medication taking (28%; n=79); most of the 252 provider medication messages referred to medication question (69%; n=173). Although medLDA could identify a majority intent within each topic, the model could not distinguish medication intents with low prevalence within either patient or provider messages. Richer feature engineering identified informative lexical-semantic patterns associated with each medication intent class. Conclusion: LDA can be an effective method for generating subgroups of messages with similar term usage and facilitate the review of topics to inform annotations. However, few training cases and shared vocabulary between intents precludes the use of LDA for fully automated deep medication intent classification.

scholarly journals Application of natural language processing methods to extract coded data from administrative data held in the Scottish Prescribing Information System

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Clifford Nangle ◽  
Stuart McTaggart ◽  
Margaret MacLeod ◽  
Jackie Caldwell ◽  
Marion Bennie
Keyword(s):  
Information System ◽  
Natural Language Processing ◽  
Natural Language ◽  
Language Processing ◽  
Drug Exposure ◽  
Drug Dose ◽  
Free Text ◽  
Wide Range ◽  
The Impact ◽  
Prescribing Information

ABSTRACT ObjectivesThe Prescribing Information System (PIS) datamart, hosted by NHS National Services Scotland receives around 90 million electronic prescription messages per year from GP practices across Scotland. Prescription messages contain information including drug name, quantity and strength stored as coded, machine readable, data while prescription dose instructions are unstructured free text and difficult to interpret and analyse in volume. The aim, using Natural Language Processing (NLP), was to extract drug dose amount, unit and frequency metadata from freely typed text in dose instructions to support calculating the intended number of days’ treatment. This then allows comparison with actual prescription frequency, treatment adherence and the impact upon prescribing safety and effectiveness. ApproachAn NLP algorithm was developed using the Ciao implementation of Prolog to extract dose amount, unit and frequency metadata from dose instructions held in the PIS datamart for drugs used in the treatment of gastrointestinal, cardiovascular and respiratory disease. Accuracy estimates were obtained by randomly sampling 0.1% of the distinct dose instructions from source records, comparing these with metadata extracted by the algorithm and an iterative approach was used to modify the algorithm to increase accuracy and coverage. ResultsThe NLP algorithm was applied to 39,943,465 prescription instructions issued in 2014, consisting of 575,340 distinct dose instructions. For drugs used in the gastrointestinal, cardiovascular and respiratory systems (i.e. chapters 1, 2 and 3 of the British National Formulary (BNF)) the NLP algorithm successfully extracted drug dose amount, unit and frequency metadata from 95.1%, 98.5% and 97.4% of prescriptions respectively. However, instructions containing terms such as ‘as directed’ or ‘as required’ reduce the usability of the metadata by making it difficult to calculate the total dose intended for a specific time period as 7.9%, 0.9% and 27.9% of dose instructions contained terms meaning ‘as required’ while 3.2%, 3.7% and 4.0% contained terms meaning ‘as directed’, for drugs used in BNF chapters 1, 2 and 3 respectively. ConclusionThe NLP algorithm developed can extract dose, unit and frequency metadata from text found in prescriptions issued to treat a wide range of conditions and this information may be used to support calculating treatment durations, medicines adherence and cumulative drug exposure. The presence of terms such as ‘as required’ and ‘as directed’ has a negative impact on the usability of the metadata and further work is required to determine the level of impact this has on calculating treatment durations and cumulative drug exposure.

scholarly journals The impact of urban-based family medicine postgraduate rotations on rural preceptors/teachers

2021 ◽  
Author(s):  
Douglas Myhre ◽  
Jodie Ornstein ◽  
Molly Whalen-Browne ◽  
Rebecca Malhi
Keyword(s):  
Family Medicine ◽  
Negative Impact ◽  
Teaching Experience ◽  
Rural Environment ◽  
Free Text ◽  
Rural Physicians ◽  
Rural Residency ◽  
Perceived Impact ◽  
Rural Teaching ◽  
The Impact

Background: The use of rural rotations within urban-based postgraduate programs is the predominant response of medical education to the health needs of underserved rural populations.  The broader impact on rural physicians who teach has not been reported. Methods: This study examined the personal, professional, and financial impact of a rural rotations for urban-based family medicine (UBFM) residents on Canadian rural teaching physicians. A survey was created and reviewed by community and academic rural physicians and a cohort of Canadian rural family physicians teaching UBFM residents was sampled. Survey data and free-text responses were assessed using quantitative and qualitative analyses.   Results: Participants with rural residency backgrounds perceived a negative impact of teaching UBFM (p = 0.02 personal and professional) and those in a primary rural environment (as defined below) perceived impact as positive (p < 0.001). Rural preceptors often held contrasting attitudes towards learners with negative judgements counter-balanced by positive thoughts. Duration in practice and of teaching experience did not have a significant impact on ratings. Conclusion: Being a rural preceptor of UBFM residents is rewarding but also stressful. The preceptor location of training and scope of practice appears to influence the impact of UBFM residents.

scholarly journals The Long-Run Effects of Attending an Elite School: Evidence from the United Kingdom

2016 ◽  
Vol 8 (1) ◽  
pp. 150-176 ◽  
Author(s):  
Damon Clark ◽  
Emilia Del Bono
Keyword(s):  
Negative Impact ◽  
School Attendance ◽  
Labor Market Outcomes ◽  
Market Outcomes ◽  
The United Kingdom ◽  
Long Run ◽  
Elite School ◽  
Significant Negative Impact ◽  
The Impact ◽  
School Assignment

This paper estimates the impact of elite school attendance on long-run outcomes including completed education, income, and fertility. Our data consist of individuals born in the 1950s and educated in a UK district that assigned students to either elite or non-elite secondary schools. Using instrumental variables methods that exploit the school assignment formula, we find that elite school attendance had large impacts on completed education. Surprisingly, there are no significant effects on most labor market outcomes except for an increase in female income. By contrast, we document a large and significant negative impact on female fertility. (JEL I21, I24, I26, J13, J16, J24, J31)

Influence of tumor site on cognitive deficits in children with cerebellar tumors at diagnosis

2006 ◽  
Vol 24 (18_suppl) ◽  
pp. 9045-9045
Author(s):  
R. Riccardi ◽  
L. Peruzzi ◽  
L. Iuvone ◽  
C. Colosimo ◽  
G. Tamburrini ◽  
...  
Keyword(s):  
Executive Functions ◽  
Language Processing ◽  
Neurological Examination ◽  
Cognitive Deficits ◽  
Negative Impact ◽  
Cognitive Outcome ◽  
Neurological Deficits ◽  
Cerebellar Hemisphere ◽  
Cerebellar Tumor ◽  
The Impact

9045 Background: Children with cerebellar tumor are at risk for cognitive deficits (CD) depending on surgery and radiotherapy. Only few studies analyze the role of tumor itself in mental functioning (Ellenberg et al., ‘87). Data about neuropsychological organization before any treatment are essential to understand the effect of tumor and have a baseline for analyzing the negative impact of the different treatments in the CD. Aim of this study is to prospectively analyze cognitive functions before treatment in patients (pts) with cerebellar tumors. Methods: Twenty-five pts with cerebellar tumor were assessed at diagnosis.Children with previous and severe neurological disturbances neurological were excluded. Intelligence quotient (IQ) and sectorial cognitive abilities (memory, attention, language, visuospatial and executive functions) were evaluated. Neurological examination (BUSPAR) and magnetic resonance imaging (MRI) were performed in the same period of cognitive assessment. Neurological deficits were classified as major, mild or absent in relation with the results of BUSPAR. Results: Twenty pts were selected; males/females: 12/8; age: 7.6 years (range: 18 m-14.8 y); histhology: pilocytic astrocytoma (9 pts), medulloblastoma (9), ependymoma (1) and atypical teratoid-rabdoid (1); tumor location: right cerebellar hemisphere (4), left (4), vermal (12); neurological examination: major neurological signs (2 pts), mild (10), absent (6); hydrocephalus: 50% of pts. Three pts had IQ values below the average level, although mean IQ values were normal (mean: 99.6; range: 78–118). Sixteen/20 pts had selective CD mainly involving working memory, executive functions, attention, and visual motor integration. Language processing was defective in 6 pts (2/4 right-sides lesions, 4/12 vermal lesion). Conclusions: Sectorial CD are present before treatment in about 80% of pts, mainly related to the location of tumor. Preliminar data suggest a correlation between specific sites inside cerebellum and selective CD, with language problems mainly in right hemispheric tumor. Complex cognitive impairment was present in 15% of pts before treatment. These data will represent the baseline for further analysis about the impact of treatment on cognitive outcome. No significant financial relationships to disclose.

EU to United Kingdom moves will fall under any Brexit

2019 ◽  
Keyword(s):  
United Kingdom ◽  
Immigration Policy ◽  
Negative Impact ◽  
Real Wages ◽  
Reduced Pressure ◽  
Content Type ◽  
Freedom Of Movement ◽  
The United Kingdom ◽  
The Impact ◽  
The Eu

Subject UK immigration outlook. Significance Under most Brexit scenarios, freedom of movement between the United Kingdom and the EU-27 will end in December 2020. This will require the United Kingdom radically to reform its immigration policy, both towards EU citizens and, to a lesser extent, non-EU citizens. Impacts Immigration reductions will have a small negative impact on UK public finances, even considering the reduced pressure on public services. There will be some increase in non-EU migration, especially of relatively well-paid workers. There may be some upward pressure on wages in specific sectors, although the overall impact on real wages is likely to be small. A change of prime minister may reduce immigration policy restrictiveness, mitigating the impact without altering the direction of travel.

scholarly journals 493 An Examination into The Impact of Sleep Deprivation on Surgeons

2021 ◽  
Vol 108 (Supplement_6) ◽  
Author(s):  
V Antoniou ◽  
C C Booth ◽  
D Parry
Keyword(s):  
Sleep Deprivation ◽  
Health Care Professionals ◽  
Negative Impact ◽  
Sleep Habits ◽  
Deprivation Score ◽  
The United Kingdom ◽  
King's College ◽  
Lack Of Sleep ◽  
Short And Long Term ◽  
The Impact

Abstract Background Lack of sleep amongst surgeons is significant and worrying. It poses short- and long-term risks to surgeons’ health and negatively impacts patient outcomes. Previous studies have examined sleep deprivation amongst health care professionals. The aim of the present study was to examine impact in a specific population of surgical doctors. Method A questionnaire-based study completed in the anatomy department of King’s College London University. Surgical subjects spanned the United Kingdom. Subjects completed 14 questions regarding sleep habits. Data was compiled, calculating a sleep deprivation score. Results Valid responses were obtained from 66 surgical subjects of varying seniority. Mean age of subjects was 33.7 years old. 59.1% of subjects had rota commitments changing on a weekly basis. Average sleep amongst subjects amounted to 6.15 (± 1.26) hours per night. Daily sleep did not present differences dependent on seniority level (p = 0.186). 25.8% of subjects took &gt;30 minutes to fall asleep. Our subjects woke 1.67 (± 1.21) times a night. Mean sleep deprivation score amongst our surgical population was 16.5 (± 4.26) demonstrating moderate negative impact on daily activities. 28.9% accumulated ≧20 sleep deprivation score demonstrating severe impact of sleep deprivation on life. Conclusions Our study has demonstrated reduced quantity and quality of sleep amongst our subject population. With protecting the health of both patients and surgeons in mind, we must place higher importance on improving sleep amongst surgical professionals.

scholarly journals Exploring the Impact of COVID-19 on Social Life by Deep Learning

Information ◽  
2021 ◽  
Vol 12 (11) ◽  
pp. 459
Author(s):  
Jose Antonio Jijon-Vorbeck ◽  
Isabel Segura-Bedmar
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Social Life ◽  
Data Science ◽  
Short Term Memory ◽  
Negative Impact ◽  
Machine Learning Algorithms ◽  
World Health ◽  
Health Organization ◽  
The Impact

Due to the globalisation of the COVID-19 pandemic, and the expansion of social media as the main source of information for many people, there have been a great variety of different reactions surrounding the topic. The World Health Organization (WHO) announced in December 2020 that they were currently fighting an “infodemic” in the same way as they were fighting the pandemic. An “infodemic” relates to the spread of information that is not controlled or filtered, and can have a negative impact on society. If not managed properly, an aggressive or negative tweet can be very harmful and misleading among its recipients. Therefore, authorities at WHO have called for action and asked the academic and scientific community to develop tools for managing the infodemic by the use of digital technologies and data science. The goal of this study is to develop and apply natural language processing models using deep learning to classify a collection of tweets that refer to the COVID-19 pandemic. Several simpler and widely used models are applied first and serve as a benchmark for deep learning methods, such as Long Short-Term Memory (LSTM) and Bidirectional Encoder Representations from Transformers (BERT). The results of the experiments show that the deep learning models outperform the traditional machine learning algorithms. The best approach is the BERT-based model.

Sign in / Sign up

Export Citation Format

Share Document