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Record W4390202740 · doi:10.1177/21582440231219658

Global Research Pattern of Cognitive Distortion: A Bibliometric Analysis

2023· article· en· W4390202740 on OpenAlexaboutno aff
Fauziah Zaiden, Mastura Mahfar, Aslan Amat Senin, Faizah Mohd Fakhruddin

Bibliographic record

VenueSAGE Open · 2023
Typearticle
Languageen
FieldPsychology
TopicMental Health Research Topics
Canadian institutionsnot available
Fundersnot available
KeywordsScopusBibliometricsCognitionDistortion (music)Mental healthData scienceField (mathematics)PsychologyApplied psychologyComputer sciencePolitical scienceLibrary scienceMEDLINEPsychiatry

Abstract

fetched live from OpenAlex

Research on the concept of cognitive distortion has increased from year to year, especially in the areas of mental health and emotional disorders such as depression, anxiety disorder, stress disorder, and others. Therefore, it is necessary to understand the topic of interest and establish a network of cooperation to expand research toward integrated efforts. Bibliometric analysis was performed using Bibliometrix and Biblioshiny software packages on academic articles indexed on the Scopus and Web of Science (WoS) databases. Search criteria were applied, initially resulting in a total of 1,834 articles published between 1950 and 2021. The study pattern focuses on publication output, affiliated countries, co-authors, and the occurrence of author keywords. The findings of this study provide an overview of notable topics in cognitive distortion analytics through a quantitative analysis featuring tables, graphs, and maps. It also identifies the key performance indicators to produce articles and their citations. Studies show an increase in this field globally, especially since 2008 onwards. The data reveals that publications from the United States, United Kingdom, and Canada accounted for more than 50% of total number of publications in this field. This study indicates a global research pattern related to cognitive distortion, which may be useful for researchers, individuals, or policymakers to harness the existing potential and create opportunities for future research development.

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
Not applicablelow
gptBibliometrics
Domain: not available · Genre: Empirical
About the Canadian research system: no · About a Canadian topic: no
Observationalhigh
models splitAgreement 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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesBibliometrics, Insufficient payload (model declined to judge)
Consensus categoriesBibliometrics, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.497
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0300.311
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0090.002

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 head0.373
GPT teacher head0.611
Teacher spread0.239 · 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.

Bibliometrics

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Observational
Domainnot available
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

Citations4
Published2023
Admission routes1
Has abstractyes

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