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PECULIARITIES OF ALEXITYMIA LEVEL AND TYPE OF ATTITUDE TOWARDS THE DISEASE AMONG STUDENTS WITH PSYCHOSOMATIC DISEASES

2025· article· en· W4409852148 on OpenAlexaboutno aff
A.B. Shagiyeva, R.Т. Alimbayeva

Bibliographic record

VenueHabaršy. Psihologiâ seriâsy · 2025
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsDiseasePsychologyMedicineClinical psychologyInternal medicine

Abstract

fetched live from OpenAlex

The article presents the results of study for peculiarities of alexitymia level and type of attitude towards disease among students with psychosomatic diseases. Urgency of the research is conditioned with the fact that modern society faces the growing problem of psychosomatic diseases, which are diagnosed among students with increasing frequency. Alexitymia influences the human’s perception of the disease and attitude towards own health in whole, which makes the treatment process complicated. As methods, we used questionnaire survey with the aim to disclose the group of patients (conditional) with psychosomatic diseases, «Toronto alexitymia scale» (TAS-26) estimation of alexitymia level and method of «Type of attitude towards disease» for diagnostics of the type of attitude towards disease among students. Significant interrelations between alexitymia level and types of attitude towards disease among students with psychosomatic diseases were disclosed. It was found that students with high alexitymia level much more frequently have psychosomatic diseases compared to students with low alexitymia level. The significance of research is in its practical opportunities for improvement of collaboration of general practitioners and psychologists, which is especially important at treatment of students with psychosomatic diseases. The understanding of interrelation peculiarities of alexitymia and type of attitude towards disease among students gives the opportunity to select the most efficient complex methods of accompaniment and treatment, and promotes the patient retention towards the therapy.

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)0.000
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 score0.005
Threshold uncertainty score0.514

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.042
GPT teacher head0.336
Teacher spread0.294 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

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