Determinants of Dispositional Mindfulness Based on Alexithymia and Positive and Negative Dimensions of Perfectionism in College Students
Bibliographic record
Abstract
Introduction: Dispositional mindfulness is an important component of psychological health. Research shows that factors such as alexithymia and perfectionism affect mindfulness. The present research was performed with the aim of investigating the role of dispositional mindfulness on alexithymia and perfectionism among students of Tabriz University in the academic year 2016-2017. Materials and Methods: in a descriptive-correlational study, 150 students from the University of Tabriz selected by using the available sampling method participated in the present research. The data were gathered using a five-dimension mindfulness questionnaire (FFMQ), a positive and negative perfectionism scale and a alexithymia scale (TAS-20). The data were analyzed using the Pearson correlation test and the linear regression analysis in the SPSS software. Result: The results showed that there is negative relationship between difficulty in identifying feelings with mindfulness (B=0/24, p=029). Also, negative perfectionism (B=/029) and positive perfectionism (B=-/027) are able to predict the variance of mindfulness in students. Conclusion: According to the obtained results, it can be concluded that difficulty in identifying feeings and perfectionism (Positive and negative) can be determinants of dispositional mindfulness in college 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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".