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THE RELATIONSHIP BETWEEN IMPULSIVITY AND ALEXITHYMIA IN A SAMPLE OF STRONG NICOTINE ADDICTED. A PRELIMINARY STUDY

2017· other· en· W6908708608 on OpenAlexaboutno aff

Bibliographic record

VenueBiblioBoard Library Catalog (Open Research Library) · 2017
Typeother
Languageen
FieldPhysics and Astronomy
TopicIonosphere and magnetosphere dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaImpulsivityToronto Alexithymia ScaleCognitionCorrelationAddictionAnxietySample (material)

Abstract

fetched live from OpenAlex

Background and Aims:Given the correlation between tobacco addiction and impulsivity, this study means to evaluate the role of alexithymia in the relationship between impulsivity and tobacco addiction. Alexithymia is defined as a difficulty in the mental representation of emotions due to a loss of integration between physiological and cognitive component of emotions. Alexithymia can be characterized by an operative kind of thought, lacking in imagination, fantasy or oneiric activity that, according to the u201cHuman Birth Theoryu201d by Massimo Fagioli, are fundamental to ensure the possibility to mentally elaborate psychic and physical sensation. Consequently, a lack of psychophysical sensibility could prejudice the imaginative thinking process, mystifying our experience awearness (Atzori, 2017), and making emotional experience less comprehensible and intense.Methods:This preliminary study examines the correlation between the dimensions of impulsivity and alexithymia in a sample of 30 help-seekers related to a Service for Addictions, diagnosed with Tobacco Addiction, through the analysis of the results of a test battery distributed at the admission.Results:Test results suggest that alexithymia has a role in leading to impulsive action. The greatest correlation was found between alexithymia and the impulsivity sub-factor called

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.340
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.003
Open science0.0030.005
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.059
GPT teacher head0.345
Teacher spread0.286 · 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.

Study designObservational
Domainnot available
GenreOther

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
Published2017
Admission routes1
Has abstractyes

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