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Record W2793777447 · doi:10.1002/ijop.12479

Alexithymia and risk preferences: Predicting risk behaviour across decision domains

2018· article· en· W2793777447 on OpenAlexaboutno aff
Angelo Panno, Ainize Sarrionandia, Marco Lauriola, Mauro Giacomantonio

Bibliographic record

VenueInternational Journal of Psychology · 2018
Typearticle
Languageen
FieldMedicine
TopicPsychosomatic Disorders and Their Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsAlexithymiaToronto Alexithymia ScalePsychologyFacet (psychology)Psychological interventionScale (ratio)Clinical psychologyFeelingImpulsivityCognitionRisk factorDevelopmental psychologySocial psychologyPersonalityPsychiatryBig Five personality traitsMedicine

Abstract

fetched live from OpenAlex

Risk-taking is a critical health factor as it plays a key role in several diseases and is related to a number of health risk factors. The aim of the present study is to investigate the role of alexithymia in predicting risk preferences across decision domains. One hundred and thirteen participants filled out an alexithymia scale (Toronto Alexithymia Scale-TAS-20), impulsivity and venturesomeness measures (I7 scale), and-1 month later-the Cognitive Appraisal of Risky Events (CARE questionnaire). The hierarchical regression analyses showed that alexithymia positively predicted risk preferences in two domains: aggressive/illegal behaviour and irresponsible academic/work behaviour. The results also highlighted a significant association of the alexithymia facet, externally oriented thinking (EOT), with risky sexual activities. EOT also significantly predicted aggressive/illegal behaviour and irresponsible academic/work behaviour. The alexithymia facet, Difficulty Identifying Feelings, significantly predicted irresponsible academic/work behaviour. The results of the present study provide interesting insights into the connection between alexithymia and risk preferences across different decision domains. Implications for future studies and applied interventions are discussed.

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 score0.075
Threshold uncertainty score0.353

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 head0.022
GPT teacher head0.387
Teacher spread0.365 · 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

Citations15
Published2018
Admission routes1
Has abstractyes

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