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Record W2056056588 · doi:10.4236/nm.2014.51009

Alexithymia, Psychopathology and Alcohol Misuse in Adolescence: A Population Based Study on 3556 Teenagers

2014· article· en· W2056056588 on OpenAlexaboutno aff
Michela Gatta, Irene Facca, Elena Colombo, Lorenza Svanellini, Sara Montagnese, Sami Schiff

Bibliographic record

VenueNeuroscience &amp Medicine · 2014
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaPsychopathologyToronto Alexithymia ScalePsychologyClinical psychologyPopulationAlcoholPsychiatryMedicineEnvironmental health

Abstract

fetched live from OpenAlex

Objectives: To analyze the association between alexithymia and alcohol intake during adolescence, also in relation to psychopathology, in order to identify psychological risk factors for alcohol misuse. Method: 3556 students [mean age (range) 14.5 years (11-18)] were recruited in the Padua area. Each was administered a set of three questionnaires: the Toronto Alexithymia Scale for children (TAS-20) to measure alexithymia, the Questionnaire Adolescent Saturday evening (QAS) to estimate of alcohol intake, and the Youth Self-Report (YSR 11-18) to value psychopathology. Results: Externalizing problems appeared to increase with age and with the amount of alcohol consumed, unlike internalizing problems. The prevalence of alexithymia was 18%, decreasing with age, and it was not associated with alcohol consumption, and used except in younger subjects (≤13), for whom a positive correlation was observed between alexithymia, internalizing problems and alcohol intake. Conclusions: Younger adolescents are more psycho-emotionally vulnerable (internalizing problems and alexithymia) and at a greater risk of alcohol misuse.

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)0.001
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 score0.006
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.044
GPT teacher head0.353
Teacher spread0.308 · 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".

Quick stats

Citations24
Published2014
Admission routes1
Has abstractyes

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