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Record W4405819503 · doi:10.1007/s12144-024-06876-8

Knowledge Atlas and emerging trends on the impact of mindfulness on tumors: a bibliometric analysis

2024· article· en· W4405819503 on OpenAlexaboutno aff
Zeqi Ji, Jinyao Wu, Huiting Tian, Qiuping Yang, Lingzhi Chen, Jiehui Cai, Daitian Zheng, Zhiyang Li, Yexi Chen

Bibliographic record

VenueCurrent Psychology · 2024
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsnot available
Fundersnot available
KeywordsPsychologyMindfulnessAtlas (anatomy)Clinical psychologyMedicine

Abstract

fetched live from OpenAlex

The affliction of tumors presents a formidable health challenge all over the world. Meanwhile, the possible therapeutic effects of mindfulness on tumors are gradually being explored. This article aims to conduct a bibliometric investigation into the potential impact of mindfulness on tumors. All related publications on mindfulness and tumors were retrieved from the Web of Science Core Collection database. Bibliometric analysis tools (Biblioshiny, Citespace, VOSviewer) and Microsoft Excel were employed to draw a knowledge atlas and emerging trends by analyzing the number of articles, authors, countries/regions, institutions, journals, articles, references, and keywords. A total of 1,125 articles on mindfulness and tumors were included from 2013 to 2022, with an annual growth rate of 15.63%. Most of these articles originated from the USA, China, and Canada. Psycho-Oncology and Mindfulness are both significant journals in this field on a global scale. Carlson LE stands out as the most influential, with the highest number of publications and citations. Breast cancer is the most studied cancer in mindfulness-based interventions. Quality of life, stress reduction, depression and anxiety are hot topics of research. Mindfulness may play an valuable role in cancer treatment. Further exploration may focus on the underlying mechanisms of mindfulness on tumors. This bibliometric analysis describes the current state and hotspots in this field and provides crucial leads for future scientific strategies and research directions.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationallow
gptBibliometrics
Domain: not available · Genre: Review
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models agreeAgreement compares identical category sets and study designs across arms.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.922
Threshold uncertainty score0.973

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0500.074
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0280.000

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 head0.088
GPT teacher head0.466
Teacher spread0.378 · 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

Labeled directly by 2 models reading the full record.

Study designObservational
Domainnot available
GenreEmpirical · Review

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
Published2024
Admission routes1
Has abstractyes

Explore more

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