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Record W4384487330

The role of alexithymia and mindfulness in predicting depression and anxiety in women with cancer

2020· article· en· W4384487330 on OpenAlexaboutno aff
Mohammad Narimani‎, Setareh Jani, Roonak Rezaei

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2020
Typearticle
Languageen
FieldPsychology
TopicHealth and Well-being Studies
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaMindfulnessAnxietyDepression (economics)Clinical psychologyPsychologyCancerPsychiatryMedicineInternal medicine
DOInot available

Abstract

fetched live from OpenAlex

Introduction: Depression and anxiety in cancer patients are associated with a variety of cognitive and personality variables such as alexithymia and mindfulness. Aim: The present study aimed to investigate the role of alexithymia and mindfulness in predicting depression and anxiety in women with cancer. Method: The research method is correlational and predictive. The statistical population consisted of all female patients with cancer covered by Arezoo Charity Institute in Pars Abad (1979). 120 women with cancer were selected through purposive sampling from the statistical population and participated in this study. The data were collected using Toronto Alexithymia Scale (TAS-20), Mindful Attention Awareness Scale (MAAS) and Hospital Anxiety and Depression Scale (HADS), and analyzed by SPSS-20 software, Pearson correlation coefficient and stepwise regression analysis. Results: The findings of this study showed that alexithymia and mindfulness can predict depression and anxiety in women with cancer significantly (P<0.01). Conclusion: Alexithymia and mindfulness are important factors in the depression and anxiety of cancer patients. Therefore, it is necessary to emphasize psychological interventions focused on these variables in educational, prevention and treatment programs of these patients.

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 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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.124
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

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

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 head0.094
GPT teacher head0.496
Teacher spread0.402 · 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 teacher head, 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

Citations1
Published2020
Admission routes1
Has abstractyes

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