ArticleAlpha psychiatry2022
Prediction of Global Psychological Stress and Coping Induced by the COVID-19 Outbreak: A Machine Learning Study.
Article in Alpha psychiatry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 3 citations in OpenAlex.
- Associations between Sleep Disturbances, Personality Traits and Self-Regulation in a Sample of Healthy Adults.Journal of clinical medicine · 2024Article
- Assist me or replace me? Uncovering the influence of AI awareness on employees' counterproductive work behaviors.Frontiers in public health · 2024Article
- Classifying the Perception of Difficult Life Tasks: Machine Learning and/or Modeling of Logical Processes.Psychology in Russia : state of the art · 2024Article
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
Background: Artificial intelligence and machine learning have enormous potential to deal efficiently with a wide range of issues that traditional sciences may be unable to address. Neuroscience, particularly psychiatry, is one of the domains that could potentially benefit from artificial intelligence and machine learning. This study aims to predict Methods: COVIDiSTRESS global survey data was used in this study and comprised 70 652 respondents after pre-processing. Binary classification is performed for predicting Results: Globally, females, the younger population, and those in COVID-19 risk groups are observed to possess higher levels of s Conclusions: By comparing different classifiers, we can conclude that multi-layer ensemble outperforms all. Another aim of this study, is the ability to regulate demographic and negative psychological states with a goal of medical interventions and to work towards building multiple coping strategies to reduce stress and promote resilience and recovery from COVID-19.
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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.