ABSTRACT
The tourism industry has dynamized the economy of the countries by offering places, as well as related tourism experiences, products, and services. In the context of the COVID-19 pandemic, some of these tourist destinations were affected by subjective perceptions of users on social networks, within stands out Twitter. To achieve an objective perception from user comments posted on Twitter in front of a tourist destination, we propose a PANAS-tDL (Positive and Negative Affect Schedule - Deep Learning) model which integrates into a single structure a neural model inspired by a Stacked neural deep learning model (SDL), as well as the PANAS-t methodology. For this process, a database of comments was available for four destinations (Colombia, Italy, Spain, USA), and its tourist's products and services, before and in the context of COVID-19 pandemic throughout the year 2020. The proposed model made it possible to generate objective perceptions of the tourist destinations and their products and services using an automatic classification of comments in each category defined by the PANAS-t methodology (11-sentiments). The results show how users' perceptions were towards the negative sentiment zone defined by this methodology, according to the evolution of the COVID-19 pandemic worldwide throughout the year 2020. The proposed model also integrated an automatic process of normalisation, lemmatisation and tokenisation (Natural language process - NLP) for the objective characterization of perceptions, and due to its capacity for adaption and learning, it can be extended for the evaluation of new tourist destinations, products or services using comments from different social networks. Copyright © 2021 by Author/s and Licensed by Veritas Publications Ltd., UK.
ABSTRACT
Background: The well-being of oncology providers (OP) is in jeopardy with increasing workload, limited resources, and personal challenges that result from the COVID-19 pandemic. We aim to evaluate the impact of COVID-19 on work-related (WR) satisfaction and fatigue among OP in Latin America. Methods:We conducted an international cross-sectional online survey of OP practicing in Latin America. The survey was administered in English, Spanish, and Portuguese. Data was analyzed using descriptive statistics and Chi-square tests. Results: In August 2020, 704 OP from 20 Latin American countries completed the survey (77% of 913 who started the survey). Table outlines baseline characteristics. Higher frequency of WR fatigue (67% vs. 58%, p=0.010) and exhaustion (81% vs. 70%, p=0.001) were reported by OP who cared for patients with COVID-19, compared to OP who cared for patients without COVID-19. Providers that observed delays in referrals to radiation (p=0.002) and surgery (p=0.04) reported WR fatigue at higher rates than their counterparts. Higher exhaustion (p=0.016) and dissatisfaction (p=0.046) were reported by OP who lacked access to supportive services, as social work. A significantly higher proportion of women reported WR fatigue (72% vs. 56%, p=0.003) and exhaustion (86% vs. 68%, p=0.001), when compared to men. Women were more likely than men to endorse higher current levels of fatigue when compared to pre-COVID-19 (61% vs. 46%, p=0.0001). To reduce stress, women were more likely than men to cut the time spent watching the news (p=0.002). Both genders declined research collaborations and speaking opportunities. Conclusions: Fatigue and dissatisfaction with work-life were prevalent among OP in Latin America. Higher rates of WR fatigue were seen in women, OP caring for patients with COVID-19, and OP with patients who experienced cancer care delays. Our data imply that OP may be a prime target for psychosocial support, particularly as current challenges will continue for the foreseen future. Baseline characteristics (N=704).
ABSTRACT
Background: The severe acute respiratory syndrome 2 (SARS-cov-2) virus causing COVID19 has brought great challenges to global health services affecting cancer care delivery, outcomes, and increasing the burden in oncology providers (OP). Our study aimed to describe the challenges that OP faced while delivering cancer care in Latin America. Methods: We conducted an international crosssectional study using an anonymous online survey in Spanish, Portuguese, and English. The questionnaire included 43 multiple choice questions. The sample was stratified by OP who have treated patients with COVID-19 versus those who have not treated patients with COVID-19. Data was analyzed with descriptive statistics and Chi-square tests. Results: A total of 704 OP from 20 Latin American countries completed the survey (77% of 913 who started the survey). Oncologists represented 46% of respondents, followed by 25% surgicaloncologists. Of the respondents, 56% treated patients with COVID-19. A significant proportion of OP reported newly adopting telemedicine during COVID-19 (14% vs 72%, p=0.001). More than half (58%) of OP reported making changes to the treatments they offered to patients with cancer. As shown in the table, caring for patients with COVID-19 significantly influenced practice patterns of OP. Access to specialty services and procedures was significantly reduced: 40% noted significantly decreased or no access to imaging, 20% significantly decreased or no access to biopsies, 65% reported delays in surgical oncology referrals, and 49% in radiation oncology referrals. A vast majority (82%) reported oncologic surgeries were delayed or cancelled, which was heightened among those treating patients with COVID-19 (87% vs 77%, p=0.001). Conclusions: The COVID-19 pandemic has significantly affected the way cancer care is delivered in globally. Although changes to healthcare delivery are necessary as a response to this global crisis, our study highlights the significant disruption and possible undertreatment of patients with cancer in Latin America that results from COVID-19.
ABSTRACT
One of the main sector that moves the economy of the countries worldwide is tourism and its associated services. Dynamics like globalization has led these countries to create tourism services with global standards, however, in the context of COVID-19 pandemic, these services have been affected as shown the social networks. This fact led to a change in the perception of tourists against a destination. In order to unify this change in an objective manner, we propose a Deep Learning model that integrates a PANAS scale (Positive and Negative Affect Scale) (PANAS-tDL), to characterize a tourist destination based on a series of potential factors (weather conditions, healthy, holidays, seasonality and economic factors) identified in comments obtained from a social network like Twitter. The results obtained by the PANAS-tDL model show its good performance evaluating the change of perception of tourists against four destinations affected by COVID-19, taking as reference the 11-sentiment scale defined by PANAS-t scale. Thanks to adaptation capacity, the model can be extended to evaluate the change in perception of tourists using different social networks and to evaluate different marketing strategies to promote a destination. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.