Metacognition Beliefs and General Health in Predicting Alexithymia in Students
Bibliographic record
Abstract
OBJECTIVES: The present study was conducted to investigate the role of metacognition beliefs and general health in alexithymia in Iranian students. METHODS: This descriptive and correlational study included 200 participants of high schools students, selected randomly from students of two cities (Sari and Dargaz), Iran. Metacognitive Strategies Questionnaire (MCQ-30); the General Health Questionnaire (GHQ) and Farsi Version of the Toronto Alexithymia Scale (TAS-20) were used for gathering the data. Using the Pearson's correlation method and regression, the data were analyzed. RESULTS: The findings indicated significant positive relationships between alexithymia and all subscales of general health. The highest correlation was between alexithymia and anxiety subscale (r=0.36, P<0.01). Also, there was a significant negative relationship between alexithymia and some metacognitive strategies. The highest significant negative relationship was seen between alexithymia and the sub-scale of risk uncontrollability (r=-0.359, P < 0.01). Based on the results of multiple regressions, three predictors explained 21% of the variance (R2=0. 21, F=7.238, P<0.01). It was found that anxiety subscale of General Health significantly predicted 13% of the variance of alexithymia (β=0.36, P<0.01) and risk uncontrollability subscale of Metacognition beliefs predicted about 8% of the variance of alexithymia (β=-0.028, P<0.01). CONCLUSIONS: The findings demonstrated that metacognition beliefs and general health had important role in predicting of alexithymia in students.
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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".