ArticleScientific reports2022
A machine learning analysis of COVID-19 mental health data.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
9 citing papers in PubMed, 46 citations in OpenAlex.
- Impact and resiliency of the early phase of the COVID-19 pandemic on healthcare providers and their household members.Scientific reports · 2025Article
- Tracing the evolutionary pathway of SARS-CoV-2 through RNA sequencing analysis.Scientific reports · 2025Article
- Evaluating Machine Learning Stability in Predicting Depression and Anxiety Amidst Subjective Response Errors.Healthcare (Basel, Switzerland) · 2024Article
- Article
- Comparison of depressive symptoms among healthcare workers in high-risk versus low-risk areas during the first month of the COVID-19 pandemic in China.Frontiers in psychiatry · 2023Article
- An intelligent framework to measure the effects of COVID-19 on the mental health of medical staff.PloS one · 2023Article
- A robust and resilience machine learning for forecasting agri-food production.Scientific reports · 2022Article
- Artificial intelligence-based analytics for impacts of COVID-19 and online learning on college students' mental health.PloS one · 2022Article
- Predictive Models for Canadian Healthcare Workers Mental Health During COVID-19.Journal of primary care & community healthArticle
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In late December 2019, the novel coronavirus (Sars-Cov-2) and the resulting disease COVID-19 were first identified in Wuhan China. The disease slipped through containment measures, with the first known case in the United States being identified on January 20th, 2020. In this paper, we utilize survey data from the Inter-university Consortium for Political and Social Research and apply several statistical and machine learning models and techniques such as Decision Trees, Multinomial Logistic Regression, Naive Bayes, k-Nearest Neighbors, Support Vector Machines, Neural Networks, Random Forests, Gradient Tree Boosting, XGBoost, CatBoost, LightGBM, Synthetic Minority Oversampling, and Chi-Squared Test to analyze the impacts the COVID-19 pandemic has had on the mental health of frontline workers in the United States. Through the interpretation of the many models applied to the mental health survey data, we have concluded that the most important factor in predicting the mental health decline of a frontline worker is the healthcare role the individual is in (Nurse, Emergency Room Staff, Surgeon, etc.), followed by the amount of sleep the individual has had in the last week, the amount of COVID-19 related news an individual has consumed on average in a day, the age of the worker, and the usage of alcohol and cannabis.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.