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Record W2889573217

Comparison between anticipatory effect of Alexithymia and Emotion regulation Difficulties (disorder) on language impairment in Schizophrenia patients.

2018· article· en· W2889573217 on OpenAlexaboutno aff
Reihane Bigdeli, Rezaie Omid

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldHealth Professions
TopicProblem Solving Skills Development
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaMedicineSchizophrenia (object-oriented programming)PsychiatryClinical psychology
DOInot available

Abstract

fetched live from OpenAlex

Background & Objective: Schizophrenia is one of the most fundamental challenges related to mental health. Linguistic disorganization and dysregulation are considered the main symptoms of schizophrenia diagnosis. The aim of the present study is to evaluate the anticipatory effect of alexithymia and emotional-dysregulation disorder on language disorders in schizophrenic patients. Material & Methods: This is a descriptive and analytic study in cross-sectional correlation method. Sample groups are from 81 patients who were selected using purposive sampling method. They are all psychotic patients that were hospitalized at the Razi psychiatric hospital of Tehran, and they are educated as high as high-school diploma in 2016. Participants completed the following questionnaires: Toronto Alexithymia-Scale (TAS-20), the Difficulties in Emotion Regulation-Scale (DERS) and the Farsi Aphasia-Test (Nilipoor). The collected data were analyzed using inferential statistics of regression analyzing data, multivariate analysis of variance and Pearson correlation coefficient through SPSS-22. Results: Results showed that the variable of alexithymia had a stronger anticipatory role in language impairments than the variable of emotional regulation among schizophrenic patients (P<0/01) was observed between these two variables and language impairments. The Results of regression analysis of these two on the subscales of language impairments showed that these variables (respectively) had the highest impact on improvised-conversation and listening-comprehension and the lowest impact on oral-expression. Also it could be concluded that about 19% of language impairments’ variance could be predicted by two variables of alexithymia and emotional dysregulation. Conclusion: Results of this study showed that alexithymia and emotional dysregulation disorder could be important psychological factors for predicting schizophrenia.

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)0.003
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.153
GPT teacher head0.572
Teacher spread0.419 · 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".

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Citations0
Published2018
Admission routes1
Has abstractyes

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