Health and Experiences During the COVID-19 Pandemic Among Children and Young People: Analysis of Free-Text Responses From the Children and Young People With Long COVID Study.

Publication date: Jan 28, 2025

The literature is equivocal as to whether the predicted negative mental health impact of the COVID-19 pandemic came to fruition. Some quantitative studies report increased emotional problems and depression; others report improved mental health and well-being. Qualitative explorations reveal heterogeneity, with themes ranging from feelings of loss to growth and development. This study aims to analyze free-text responses from children and young people participating in the Children and Young People With Long COVID study to get a clearer understanding of how young people were feeling during the pandemic. A total of 8224 free-text responses from children and young people were analyzed using InfraNodus, an artificial intelligence-powered text network analysis tool, to determine the most prevalent topics. A random subsample of 411 (5%) of the 8224 responses underwent a manual sentiment analysis; this was reweighted to represent the general population of children and young people in England. Experiences fell into 6 main overlapping topical clusters: school, examination stress, mental health, emotional impact of the pandemic, social and family support, and physical health (including COVID-19 symptoms). Sentiment analysis showed that statements were largely negative (314/411, 76. 4%), with a small proportion being positive (57/411, 13. 9%). Those reporting negative sentiment were mostly female (227/314, 72. 3%), while those reporting positive sentiment were mostly older (170/314, 54. 1%). There were significant observed associations between sentiment and COVID-19 status as well as sex (P=. 001 and P

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Concepts Keywords
Pandemic Adolescent
School AI
Sex artificial intelligence
Child
COVID-19
COVID-19
discourse analysis
Emotions
England
experiences
Female
free-text responses
Humans
InfraNodus
long COVID
Male
Mental Health
Pandemics
SARS-CoV-2
sentiment analysis
Social Support
Stress, Psychological
text mining

Semantics

Type Source Name
disease MESH COVID-19 Pandemic
disease MESH Long COVID
disease MESH depression
drug DRUGBANK Ademetionine
disease MESH Infection
disease MESH phobias
disease MESH anxiety
disease MESH autism
disease MESH influenza
disease MESH hay fever
disease MESH allergies
disease MESH chronic fatigue syndrome
drug DRUGBANK Acetylsalicylic acid
disease MESH glandular fever
disease IDO algorithm
drug DRUGBANK Methionine
disease MESH privacy
drug DRUGBANK Phenindione
disease MESH uncertainty
disease MESH mental illnesses
disease MESH oppositional defiant disorder
disease MESH obsessive compulsive disorder
disease MESH eating disorders
disease MESH anxiety disorder
drug DRUGBANK Tropicamide
disease MESH loneliness
disease MESH Stress Psychological

Original Article

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