Alexithymia and behavioral disorders as risk factors for alcohol misuse in adolescence.
Bibliographic record
Abstract
Introduction\nAlcohol is the psychoactive substance most used by adolescents, lately their alcohol abuse has become internationally a\nserious public health concern. Psychobehavioral factors research relating to alcohol abuse has led to various studies identifying several\npsychopathological disorders and alexithymic traits in adults.This relationship has to be analyzed in adolescents however.\nMethod\nThe present study aimed to assess alcohol consumption and its possible relationship with psychological vulnerability, in terms\nof psychobehavioral problems and alexithymia. The study was conducted on a sample of 1466 pupils attending secondary school\n(grades 6 to 13), consisting of 53,6% males and 46,4 % females, with a mean age of 13,5 years ± 1,7 SD. These adolescents were\nadministered the Adolescents’ Saturday Nights Questionnaire to quantify their alcohol consumption, the Toronto Alexithymic Scale for\ndevelopmental age to identify any alexithymic traits, the Strengths and Difficulties Questionnaire, and the Youth Self-Report 11-18 to\ndetect any psychobehavioral problems.\nResults\nMales drank more alcohol than females, and their consumption increased with age. Alexithymia was more widespread and\nstable among females, and tended to increase from 11 to 13 years old, then decreased gradually from 14 to 17 years old. An\nassociation between alcohol consumption and psychobehavioral problems was identified in the whole sample, while a statistically\nsignificant correlation between alcohol consumption and alexithymia only emerged in the subsample of 6th- to 8th-graders. Among this\nyounger group, those returning the highest scores for alexithymia and psychobehavioral problems also reported significantly higher\nalcohol consumption than their peers without such psychological issues.\nConclusions\nAlexithymic traits are a risk factor for alcohol consumption in preadolescence, especially when associated with\npsychobehavioral problems. Preventive measures designed for such young adolescents should therefore concentrate on improving\ntheir emotional awareness
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".