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Record W4413089478 · doi:10.32388/jwyqie.3

Research Trends in Mindfulness for Adolescents: Based on CiteSpace Visualization Analysis

2025· article· W4413089478 on OpenAlexaff
Fang Ye, Yali Zhang, Lilan Luo, Jialin Jin, Ke Jiang, Huilin Qiu, Ping Xu

Bibliographic record

VenueQeios · 2025
Typearticle
Language
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMindfulnessChinaWeb of scienceBibliometricsPsychologyGeographyPolitical scienceLibrary scienceMEDLINEClinical psychologyComputer scienceArchaeology

Abstract

fetched live from OpenAlex

Mindfulness has been increasingly used to improve the mental health of adolescents. This study focused on evaluating the latest research status of mindfulness for adolescents through CiteSpace and on identifying research hotspots and frontiers. We extracted English literature on mindfulness for adolescents from the Web of Science (WoS) and Chinese literature from the China National Knowledge Infrastructure (CNKI) databases, covering the period from 1999 to 2022. A total of 1317 papers were obtained. CiteSpace was used to generate online maps of worldwide cooperation among countries, institutions, and authors. Hotspots and frontiers were systematically summarized. There is a paucity of collaboration among institutions in the Chinese literature compared to the English literature. The research themes in the literature of the two languages overlap and also exhibit discrepancies at different times. Future research in mindfulness for adolescents may focus on the mechanisms and applications. Collaboration among authors should be strengthened.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.040
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Bibliometrics
Consensus categoriesnone
DomainCandidate signal: Evaluation · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.991
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.040
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.1070.089
Science and technology studies0.0020.001
Scholarly communication0.0060.006
Open science0.0010.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.467
Teacher spread0.400 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designNot applicable
DomainEvaluation
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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