Impact of Mindfulness and Alexithymia on Self-Concept: A Comprehensive Cross-Sectional Analysis
Bibliographic record
Abstract
This research aimed to explore the predictive impact of mindfulness and alexithymia on individuals' self-concept. Utilizing a cross-sectional study design, data were collected from 400 participants through standardized instruments: the Five Facet Mindfulness Questionnaire (FFMQ) for assessing mindfulness, the Toronto Alexithymia Scale (TAS-20) for measuring alexithymia, and the Self-Description Questionnaire III (SDQIII) for evaluating self-concept. Statistical analysis, including descriptive statistics and multiple linear regression, was performed using SPSS version 27 to determine the predictive relationships between the variables. The analysis revealed that mindfulness and alexithymia significantly predict self-concept. Specifically, higher levels of mindfulness were associated with a more positive self-concept, whereas elevated alexithymia levels correlated with a more negative self-concept. The model accounted for 37% of the variance in self-concept scores, indicating a strong influence of these psychological constructs on individual self-perception. The study highlights the critical roles of mindfulness and alexithymia in determining self-concept. It suggests that mindfulness interventions could be particularly beneficial for individuals with high alexithymia levels, potentially aiding in the development of a healthier self-concept. These findings offer valuable insights for psychological practice and underscore the importance of addressing both mindfulness and alexithymia in therapeutic settings.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".