Evidence map›Paper›PMID 38600124›Full record

ArticleScientific reports2024

Identifying the top predictors of student well-being across cultures using machine learning and conventional statistics.

Ronnel B King, Yi Wang, Lingyi Fu, Shing On Leung

Abstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing 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

11 citing papers in PubMed.

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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

4 authors.

Ronnel B KingDepartment of Curriculum and Instruction, Faculty of Education, The Chinese University of Hong Kong, Hong Kong, China. rbking@cuhk.edu.hk.ORCID 0000-0003-1723-1748
Yi WangFaculty of Education, University of Macau, Taipa, Macau SAR, China. wangyi_niska@163.com.ORCID 0000-0002-2065-5755
Lingyi FuDepartment of Health and Kinesiology, College of Health, University of Utah, Salt Lake City, UT, USA.ORCID 0000-0002-2256-6414
Shing On LeungFaculty of Education, University of Macau, Taipa, Macau SAR, China.ORCID 0000-0002-0962-339X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Alongside academic learning, there is increasing recognition that educational systems must also cater to students' well-being. This study examines the key factors that predict adolescent students' subjective well-being, indexed by life satisfaction, positive affect, and negative affect. Data from 522,836 secondary school students from 71 countries/regions across eight different cultural contexts were analyzed. Underpinned by Bronfenbrenner's bioecological theory, both machine learning (i.e., light gradient-boosting machine) and conventional statistics (i.e., hierarchical linear modeling) were used to examine the roles of person, process, and context factors. Among the multiple predictors examined, school belonging and sense of meaning emerged as the common predictors of the various well-being dimensions. Different well-being dimensions also had distinct predictors. Life satisfaction was best predicted by a sense of meaning, school belonging, parental support, fear of failure, and GDP per capita. Positive affect was most strongly predicted by resilience, sense of meaning, school belonging, parental support, and GDP per capita. Negative affect was most strongly predicted by fear of failure, gender, being bullied, school belonging, and sense of meaning. There was a remarkable level of cross-cultural similarity in terms of the top predictors of well-being across the globe. Theoretical and practical implications are discussed.

Indexed as

Resilience, PsychologicalStudentsAdolescentHumansMachine LearningSchoolsLife satisfactionMachine learningNegative affectPositive affectProgramme for International Student AssessmentSubjective well-being

Identifiers

PMID38600124
PMCPMC11006657

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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.