| Literature DB >> 35682477 |
Ortal Slobodin1, Ilia Plochotnikov2,3, Idan-Chaim Cohen4, Aviad Elyashar3,5, Odeya Cohen6, Rami Puzis2,3.
Abstract
BACKGROUND: Healthcare professionals (HCPs) are on the frontline of fighting the COVID-19 pandemic. Recent reports have indicated that, in addition to facing an increased risk of being infected by the virus, HCPs face an increased risk of suffering from emotional difficulties associated with the pandemic. Therefore, understanding HCPs' experiences and emotional displays during emergencies is a critical aspect of increasing the surge capacity of communities and nations.Entities:
Keywords: COVID-19; emotions; health and politics; healthcare professionals; natural language processing; twitter analysis
Mesh:
Year: 2022 PMID: 35682477 PMCID: PMC9180346 DOI: 10.3390/ijerph19116895
Source DB: PubMed Journal: Int J Environ Res Public Health ISSN: 1660-4601 Impact factor: 4.614
List of professions and specializations of HCP points of interest.
| Professions | Specializations |
|---|---|
| Anesthesiologist Assistant, Cardiovascular Technologist, Dialysis Technician, Emergency Medical Technician, Flight Nurse, Medical Laboratory Technician, Midwife, Nurse Anesthetist, Nurse Practitioner, Paramedic, Pharmacist, Phlebotomist, Physical Therapist, Physician Assistant, Radiation Therapist, Respiratory Therapist | Anesthesiology, Critical Care, Dermatology, Dermatopathology, Emergency Medicine, Family Medicine, Gynecology, Hepatology, Immunology, Internal Medicine, Neurology, Obstetrics, OB/GYN, Oncology, Ophthalmology, Pathology, Pediatrics, Pharmacy, Psychiatry, Radiology, Surgery, Urology, General Practitioner |
Figure 1Steps of study methodology.
The characteristics of the study population across the study period (2019–2020).
| Statistics | US | UK |
|---|---|---|
| Number of accounts | 14,113 | 11,094 |
| Number of tweets | 5,091,519 | 3,044,787 |
| Average number of tweets | 361 | 274 |
| Average number of friends | 626 | 515 |
| Average number of followers | 638 | 486 |
| Total tweets published in 2019 | 2,203,328 | 1,381,416 |
| Total tweets published in 2020 | 2,888,191 | 1,663,371 |
Topic of discourse, prevalence, coherence, and average sentiment score in 2020.
| Topic | Prevalence | Sentiment Score | ||||||
|---|---|---|---|---|---|---|---|---|
| US | UK | US | UK | |||||
| 95% CI | 95% CI | |||||||
| Mean | Lower | Upper | Mean | Lower | Upper | |||
| Public health and social values | 26.1% | 26% | 0.1 | 0.098 | 0.101 | 0.159 | 0.158 | 0.161 |
| Day-to-day life | 24.8% | 32.6% | 0.238 | 0.237 | 0.24 | 0.338 | 0.336 | 0.339 |
| Food | 15.5% | 10.3% | 0.056 | 0.055 | 0.058 | 0.189 | 0.186 | 0.192 |
| Politics | 8% | 3.2% | 0.023 | 0.02 | 0.025 | 0.086 | 0.082 | 0.091 |
| Professional achievements | 8.4% | 14.2% | 0.533 | 0.531 | 0.535 | 0.608 | 0.606 | 0.609 |
| Medical studies and COVID-19 information | 8.7% | 6% | 0.098 | 0.096 | 0.1 | 0.129 | 0.126 | 0.133 |
| Loss and consolation | 0.9% | 1% | 0.091 | 0.084 | 0.099 | 0.129 | 0.12 | 0.137 |
| Account promotion | 1% | 1.2% | 0.435 | 0.431 | 0.44 | 0.509 | 0.504 | 0.514 |
| Picture challenges | 1.5% | 2.1% | 0.474 | 0.469 | 0.478 | 0.53 | 0.526 | 0.535 |
The conditional probability of a tweet belonging to a topic given that it belongs to the topic of politics.
| Given (A): | Politics | |
|---|---|---|
| Subtopic (A): | US | UK |
| Public health and social values | 0.155 | 0.151 |
| Day-to-day life | 0.175 | 0.216 |
| Food | 0.139 | 0.107 |
| Professional achievements | 0.075 | 0.120 |
| Medical studies and COVID-19 information | 0.049 | 0.041 |
Figure 2Daily new confirmed COVID-19 cases (per million) in the US and UK in 2020 [33].
Figure 3Discourse trends over time—number of tweets per week by US and UK HCPs in 2020.
Mean values emotions for US and UK HCPs pre- and mid-pandemic.
| US | UK |
| US | UK |
| |
|---|---|---|---|---|---|---|
| Mean ( | Mean ( | Mean ( | Mean ( | |||
| Fear | 0.187 (0.004) | 0.174 (0.007) | 0.196 (0.007) | 0.181 (0.008) | ||
| Anger | 0.052 (0.001) | 0.045 (0.001) | 0.053 (0.002) | 0.047 (0.002) | ||
| Sadness | 0.101 (0.003) | 0.085 (0.003) | 0.106 (0.003) | 0.092 (0.003) | ||
| Joy | 0.373 (0.007) | 0.408 (0.008) | 0.355 (0.007) | 0.389 (0.01) | ||
| Surprise | 0.262 (0.005) | 0.27 (0.005) | 0.265 (0.007) | 0.27 (0.005) | ||
| Disgust | 0.024 (0.001) | 0.019 (0.001) | 0.025 (0.001) | 0.02 (0.001) |
Mean values of emotions pre- and mid-pandemic by US and UK HCPs.
| US | UK | |||||
|---|---|---|---|---|---|---|
| Mean ( | Mean ( |
| Mean ( | Mean ( |
| |
| Fear | 0.187 (0.004) | 0.196 (0.007) | 0.174 (0.007) | 0.181 (0.008) | ||
| Anger | 0.052 (0.001) | 0.053 (0.002) | 0.045 (0.001) | 0.047 (0.002) | ||
| Sadness | 0.101 (0.003) | 0.106 (0.003) | 0.085 (0.003) | 0.092 (0.003) | ||
| Joy | 0.373 (0.007) | 0.355 (0.007) | 0.408 (0.008) | 0.389 (0.01) | ||
| Surprise | 0.262 (0.005) | 0.265 (0.007) | 0.27 (0.005) | 0.27 (0.005) | ||
| Disgust | 0.024 (0.001) | 0.025 (0.001) | 0.019 (0.001) | 0.02 (0.001) | ||
Figure 4Average weekly emotion values in 2020. Note: the horizontal lines represent the mean score for each country in 2019 (solid—US, dashed—UK). 1—first wave of COVID-19 in the UK and the US; 2—the George Floyd incident; 3—second wave of COVID-19 in the US; 4—second wave of COVID-19 in the UK; 5—US elections.
Correlation between the pandemic development and emotions.
| Emotion | Virus Reproduction Rate | ∆Death | |||
|---|---|---|---|---|---|
| US | UK | US | UK | ||
| Fear | Correlation | 0.370 | 0.456 | ||
| Significance | |||||
| Lag | +1 week | +2 weeks | |||
| Anger | Correlation | 0.315 | 0.319 | −0.485 | |
| Significance | |||||
| Lag | No lag | No lag | +4 weeks | ||