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Record W4206144778 · doi:10.1111/ppc.13027

The associations between metacognition problems, mindfulness, and internalizing symptoms in university students quarantined due to Covid‐19 in Turkey

2022· article· en· W4206144778 on OpenAlexaff
Ezgi Şen Demirdöğen, Irem Orak, Özlem Merve Cansever Güzeloğlu, Nishchint Warikoo, Mesut Yavuz

Bibliographic record

VenuePerspectives In Psychiatric Care · 2022
Typearticle
Languageen
FieldPsychology
TopicMindfulness and Compassion Interventions
Canadian institutionsChild, Adolescent and Family Mental Health
Fundersnot available
KeywordsMindfulnessMetacognitionAnxietyPsychologyCoronavirus disease 2019 (COVID-19)CurfewClinical psychologyDepression (economics)Psychological interventionPandemicPsychiatryMedicineCognitionDisease

Abstract

fetched live from OpenAlex

PURPOSE: To investigate the associations between internalizing problems, metacognition problems, and mindfulness levels in university students during the COVID-19 pandemic. DESIGN AND METHODS: Depression, anxiety, and stress levels were compared between university students quarantined in dormitories and their homes under curfew conditions. The direct and indirect associations between internalizing problems, metacognition problems, and mindfulness levels of students under quarantine were evaluated. FINDINGS: Metacognition problems and low mindfulness levels were associated with higher depression, anxiety, and stress symptoms under quarantine conditions. PRACTICE IMPLICATIONS: Therapeutic interventions focusing on metacognition problems and mindfulness levels may reduce the negative psychological effects of quarantine.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.016

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.024
GPT teacher head0.340
Teacher spread0.316 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations7
Published2022
Admission routes1
Has abstractyes

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