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Record W2351572015

Mental Health and Alexithymia of Technical School Student Abused in Childhood.

2006· article· en· W2351572015 on OpenAlexaboutno aff
Zhu Xiang

Bibliographic record

VenueZhongguo xinli weisheng zazhi · 2006
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaToronto Alexithymia ScalePsychoticismPsychologyHostilitySomatizationClinical psychologyMental healthPsychiatryAnxietyPsychological abuseParanoiaSymptom Checklist 90Sexual abuseMedicinePoison controlInjury preventionPersonalityBig Five personality traits
DOInot available

Abstract

fetched live from OpenAlex

Objective: To study the effect of childhood abuse on students mental health and alexithymia, and to analyze the relationship between psychosomatic symptoms and alexithymia. Methods: 90 abused Technical Secondary School Students and 90 no-abused Technical Secondary School Students from Xuzhou were investigated by applying Symptom Checklist 90 ( SCL - 90 ) , Toronto alexithymia Scale ( TAS) . Results: Compared with no-abused students, the students who had been abused in childhood showed significantly higher psychiatric symptoms of somatization (1. 8±0. 6) , interpersonal relationship (2.0±0.7) , depression (1.9±0.6) , anxiety (1. 8±0. 5 ) , hostility (1. 8±0. 6) , paranoia (1. 9±0. 6) , psychoticism (1. 8±0. 6) , obsessive (2. 0±0. 6) , phobia (1. 7±0. 6) than control (1.4±0.5, 1.7±0.6, 1.6±0.6, 1.6±0.5, 1.5±0.5, 1.6±0.5, 1.6±0.5, 1.8±0.6, 1.5±0.5, P 0. 01 or P 0. 05) , and had significant higher total and factor scores of Toronto alexithymia Scale ( TAS) (78. 4±8. 2/68. 3±8. 1, P 0. 01 ) . The study showed that alexithymia was associated with psychiatric symptom ( r = - 0.24-0.25, P0. 05) . Conclusion: Childhood abuse can severely impact students'mental health and produce alexithymia, there were significant associations between them. Alexithymia may be one of core psychological characteristics for abused students.

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 score0.013
Threshold uncertainty score0.720

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.274
Teacher spread0.268 · 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

Citations5
Published2006
Admission routes1
Has abstractyes

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