Evidence map›Paper›PMID 39882116›Full record

ArticleFrontiers in public health2024

A cross-sectional study of parental perspectives on children about COVID-19 and classification using machine learning models.

Fahmida Kousar, Arshiya Sultana, Marwan Ali Albahar, Manoj Shamkuwar, Md Belal Bin Heyat, Mohd Ammar Bin Hayat, Saba Parveen, John Irish G Lira, Khaleequr Rahman, Abdullah Alammari and 1 more

Abstract read
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Article in Frontiers in public health, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors.

Fahmida Kousar *Department of Amraze Atfal, A and U Tibbia College & Hospital, Delhi University, New Delhi, India.
Arshiya Sultana *Department of Ilmul Qabalat wa Amraze Niswan, National Institute of Unani Medicine, Ministry of AYUSH, Bengaluru, Karnataka, India.
Marwan Ali AlbaharComputer Science Department, Umm Al-Qura University, Mecca, Saudi Arabia.
Manoj ShamkuwarDepartment of Panchkarma, A and U Tibbia College & Hospital, Delhi University, New Delhi, India.
Md Belal Bin Heyat *CenBRAIN Neurotech Center of Excellence, School of Engineering, Westlake University, Hangzhou, Zhejiang, China.
Mohd Ammar Bin Hayat *College of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, China.
Saba Parveen *College of Electronics and Information Engineering, Shenzhen University, Shenzhen, China.
John Irish G LiraNational University Manila, Manila, Philippines.
Khaleequr RahmanDepartment of Ilmul Saidla, National Institute of Unani Medicine, Ministry of AYUSH, Government of India, Bengaluru, Karnataka, India.
Abdullah AlammariFaculty of Education, Curriculums and Teaching Department, Umm Al-Qura University, Makkah, Saudi Arabia.
Eram SayeedTriveni Rai Kisan Mahila Mahavidyalaya, D. D. U. Gorakhpur University, Kushinagar, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and objective: This study delves into the parenting cognition perspectives on COVID-19 in children, exploring symptoms, transmission modes, and protective measures. It aims to correlate these perspectives with sociodemographic factors and employ advanced machine-learning techniques for comprehensive analysis. Method: Data collection involved a semi-structured questionnaire covering parental knowledge and attitude on COVID-19 symptoms, transmission, protective measures, and government satisfaction. The analysis utilised the Generalised Linear Regression Model (GLM), K-Nearest Neighbours (KNN), Support Vector Machine (SVM), Random Forest (RF), Naive Bayes (NB), and AdaBoost (AB). Results: The study revealed an average knowledge score of 18.02 ± 2.9, with 43.2 and 52.9% of parents demonstrating excellent and good knowledge, respectively. News channels (85%) emerged as the primary information source. Commonly reported symptoms included cough (96.47%) and fever (95.6%). GLM analysis indicated lower awareness in rural areas ( Conclusion: This study offers valuable insights into parental attitudes towards COVID-19 in children, highlighting symptom recognition, transmission awareness, and preventive practices. Correlating these insights with sociodemographic factors underscores the need for tailored educational initiatives, particularly in rural areas, and for addressing gender and socioeconomic disparities. The efficacy of advanced analytics, exemplified by the SVM classifier, underscores the potential for informed decision-making in public health communication and targeted interventions, ultimately empowering parents to safeguard their children's well-being amidst the ongoing pandemic.

Indexed as

COVID-19Health Knowledge, Attitudes, PracticeMachine LearningParentsAdultChildCross-Sectional StudiesFemaleHumansMaleSARS-CoV-2Surveys and Questionnaireschildren healthhealthcarekidmedical intelligencemedical machine learningparentspublic healthSARS CoV-2

Identifiers

PMID39882116
PMCPMC11776296

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

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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.