Alexithymia and risk preferences: Predicting risk behaviour across decision domains
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".