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Childhood Abuse and Neglect as a Risk Factor for Alexithymia in Adult Male Substance Dependent Inpatients

2009· article· en· W1983295852 on OpenAlexaboutno aff
Cüneyt Evren, Bilge Evren, Ercan Dalbudak, Başak Özçelik, Fatih Öncü

Bibliographic record

VenueJournal of Psychoactive Drugs · 2009
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaNeglectPsychiatryPsychologyRisk factorSubstance abuseClinical psychologyChildhood abuseChild abuseMedicineInjury preventionPoison controlMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

The prevalence of childhood abuse and neglect (CAN) histories and their associations with alexithymia among male substance-dependent inpatients were studied. Participants were 159 consecutively admitted male substance dependents (115 alcohol and 44 other drugs). Substance dependence was diagnosed by means of the Structured Clinical Interview for DSM-IV (SCID-I), Turkish version. Patients were investigated with the Toronto Alexithymia Scale (TAS-20) and Childhood Abuse and Neglect Questionnaire. Among substance-dependent patients, 57.0% had at least one type of CAN and 45.3% were considered as alexithymic since they had a score greater than 60 on the TAS-20. Rate of unemployment, low educational status, emotional abuse and history of suicide attempts were higher in alexithymic substance dependent patients. Those who had histories of two or more types of childhood abuse or neglect had also higher mean score on TAS-20, particularly on the item "difficulty in identifying feelings-DIF." Also, the number of childhood trauma types was positively correlated with TAS-20 and DIF and the "difficulty in describing feelings-DDF" items of TAS-20. History of childhood emotional abuse was the only determinant for alexithymia. Childhood emotional abuse might be a risk factor for alexithymia among inpatient substance dependents.

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 score0.296
Threshold uncertainty score0.625

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 head0.008
GPT teacher head0.275
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

Citations77
Published2009
Admission routes1
Has abstractyes

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