0000000000352738

AUTHOR

Mahmoud Elsherif

0000-0002-0540-3998

showing 2 related works from this author

Contextual factors predicting compliance behavior during the COVID-19 pandemic: A machine learning analysis on survey data from 16 countries.

2022

Voluntary isolation is one of the most effective methods for individuals to help prevent the transmission of diseases such as COVID-19. Understanding why people leave their homes when advised not to do so and identifying what contextual factors predict this non-compliant behavior is essential for policymakers and public health officials. To provide insight on these factors, we collected data from 42,169 individuals across 16 countries. Participants responded to items inquiring about their socio-cultural environment, such as the adherence of fellow citizens, as well as their mental states, such as their level of loneliness and boredom. We trained random forest models to predict whether someo…

MultidisciplinaryPhysical Distancingsocial distancingCOVID-19:Ciências Sociais::Psicologia [Domínio/Área Científica]lockdownMachine Learningvoluntary isolationCommunicable Disease ControlHumansmulti-national studySettore M-PSI/05 - Psicologia SocialePandemicsrandom forestPloS one
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Phonological precision for word recognition in skilled readers

2019

According to the lexical quality hypothesis (Perfetti, 2007), differences in the orthographic, semantic, and phonological representations of words will affect individual reading performance. Whilst several studies have focused on orthographic precision and semantic coherence, few have considered phonological precision. The present study used a suite of individual difference measures to assess which components of lexical quality contributed to competition resolution in a masked priming experiment. The experiment measured form priming for word and pseudoword targets with dense and sparse neighbourhoods in 84 university students. Individual difference measures of language and cognitive skills …

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