The Mediating Role of Alexithymia in the Relationship Between Defense Mechanisms and Tendency to High-risk Behaviors Among Adolescents
Bibliographic record
Abstract
Background: Although high-risk behaviors lead to adverse physical, psychological, and sociological consequences, less attention has been paid to identifying their related factors. The aim of this study was to investigate the mediating role of alexithymia in the relationship between defense mechanisms and high-risk behaviors among adolescents in Zahedan. Methods: In this descriptive-correlative study, junior and senior high school students of Zahedan, Iran were studied in the academic year 2015-2016. A sample of 250 (125 males and 125 females) students were chosen by multi-stage cluster sampling and asked to complete the Defense Style Questionnaire (DSQ), Toronto Alexithymia Scale (TAS), and the Risk-Taking Scale (IARS) for Iranian Adolescents. Data analysis was conducted by measuring coefficients of correlation and performing a path analysis. Results: Path analysis showed a significant correlation between defense mechanisms and alexithymia (P<0.01) and a significant correlation was found between immature defense mechanisms and high-risk behaviors (P<0.01). Conclusion: In the relationship between dysfunctional defense mechanisms and high-risk activities, alexithymia played a mediating role. It can be inferred that dysfunctional defense mechanisms play a key role in high-risk activities by influencing alexithymia.
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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 | 0.002 |
| 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".