Metacognitive thinking skills as indicators of mental health: Possible effects of alexithymia levels
Bibliographic record
Abstract
This study aimed to examine the potential effects of alexithymia levels on metacognitive thinking skills among university students by identifying the prevalence levels of alexithymia, determining the level of metacognitive thinking skills, and examining the effects of alexithymia levels on metacognitive thinking skills that are indicators of mental health. A descriptive and comparative research design was adopted to analyze behavioral patterns related to alexithymia and metacognition. The sample included 120 male and female university students randomly selected from the population. The Toronto Alexithymia Scale and the Metacognitive Thinking Scale were administered to measure the main variables. The results revealed that students exhibited generally high levels of both alexithymia and metacognitive thinking. However, significant differences were observed among groups; students with low alexithymia levels demonstrated superior metacognitive thinking skills compared to those with medium and high levels. These findings highlight the inverse relationship between alexithymia and metacognitive capacity and that alexithymia affects metacognitive thinking skills, which are important indicators of a learner's mental health. Alexithymia negatively affects students’ ability to monitor, evaluate, and regulate their own cognitive processes, which may have implications for their overall mental health and academic performance. The study recommends implementing psychoeducational programs that enhance emotional awareness, imaginative engagement, and reflective practices to strengthen metacognitive thinking and support students’ psychological well-being.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".