Evidence map›Paper›PMID 40313142›Full record

ArticleChild health nursing research2025

Predictive factors of adolescents' happiness: a random forest analysis of the 2023 Korea Youth Risk Behavior Survey.

Eun Joo Kim, Seong Kwang Kim, Seung Hye Jung, Yo Seop Ryu

Abstract read
In one paragraph

Article in Child health nursing research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

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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

2 citing papers in PubMed.

  1. Article
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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.

Eun Joo KimAssociate Professor, Department of Nursing, Gangneung-Wonju National University, Wonju, Korea.
Seong Kwang KimPhD Student, Department of Nursing, Gangneung-Wonju National University, Wonju, Korea.
Seung Hye JungPhD Student, Department of Nursing, Gangneung-Wonju National University, Wonju, Korea.
Yo Seop RyuMaster of Science in Nursing Student, Department of Nursing, Gangneung-Wonju National University, Wonju, Korea.

Funding

National Research Foundation of Korea 2021R1A2C1095530
6 · The paper itself

Abstract

purposeThis study aimed to identify predictive factors affecting adolescents' subjective happiness using data from the 2023 Korea Youth Risk Behavior Survey. A random forest model was applied to determine the strongest predictive factors, and its predictive performance was compared with traditional regression models.

methodsResponses from a total of 44,320 students from grades 7 to 12 were analyzed. Data pre-processing involved handling missing values and selecting variables to construct an optimal dataset. The random forest model was employed for prediction, and SHAP (Shapley Additive Explanations) analysis was used to assess variable importance.

resultsThe random forest model demonstrated a stable predictive performance, with an R2 of .37. Mental and physical health factors were found to significantly affect subjective happiness. Adolescents' subjective happiness was most strongly influenced by perceived stress, perceived health, experiences of loneliness, generalized anxiety disorder, suicidal ideation, economic status, fatigue recovery from sleep, and academic performance.

conclusionThis study highlights the utility of machine learning in identifying factors influencing adolescents' subjective happiness, addressing limitations of traditional regression approaches. These findings underscore the need for multidimensional interventions to improve mental and physical health, reduce stress and loneliness, and provide integrated support from schools and communities to enhance adolescents' subjective happiness.

Indexed as

AdolescentsHappinessMental healthRandom forest

Identifiers

PMID40313142
PMCPMC12056255

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LicenceCC BY-NC-ND
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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.