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Record W2371947500

Relationship of schizophrenic depressive symptoms with alexithymia and self-rating health

2010· article· en· W2371947500 on OpenAlexaboutno aff
Zeng Zhang

Bibliographic record

VenueJournal of psychiatry · 2010
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaDepression (economics)Schizophrenia (object-oriented programming)Toronto Alexithymia ScaleRating scaleClinical psychologyMoodPsychologyPsychiatryMedicine
DOInot available

Abstract

fetched live from OpenAlex

Objective To explore the characteristics of depressive mood and related factors in patients with schizophrenia.Methods A total of 320 patients with schizophrenia and 98 major depression were assessed with a self-designed questionnaire,Self-Rating Depression Scale and Depression Status Inventory(SDS),Toronto Alexithymia Scale(TAS) and Self-rating Health Measurement Scale Version 1.0(SRHMS).Correlation and regression analysis were carried out.Results 304 schizophrenic patients completed questionaires,in which 140 patients had SDS total score over 40.Compared with major depressive patients,patients with schizophrenia showed significantly lower total score of SDS,scores of psych-affective symptoms and psychomotor disturbance(P0.01).There were 15 factors such as the medical burden and condition of self-related health showed significant correlation with total score of SDS in schizophrenics(r=0.133~0.479,P0.05 or P0.01).SRHMS total score,TAS total score,hospitalized times,medical burden and family history entered the regression equation for total score of SDS in patients with schizophrenia by turns.Conclusion Depressive symptoms are very common in patients with schizophrenia,and the severity is milder than that of major depression.The self-rating health,alexithymia and medical burden are responsible for the severity of depressive mood in patients with 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 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 score0.040
Threshold uncertainty score0.271

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 studies0.0000.000
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 head0.007
GPT teacher head0.270
Teacher spread0.263 · 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
Published2010
Admission routes1
Has abstractyes

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