How Mindfulness and Acceptance Could Help Psychiatrists Predict Alexithymia Among Students
Bibliographic record
Abstract
ABSTRACT: Mindfulness and acceptance have demonstrated associations with alexithymia facets. As a very limited body of research has explored the predictive strength among alexithymia-related constructs, this study aimed to investigate the prediction of alexithymia based on acceptance and mindfulness among students. The study group consisted of 586 university students, 237 (40.9%) females and 349 (59.1%) males. As for data collection, the five-factor mindfulness questionnaire, Acceptance and Commitment Questionnaire, and the Toronto Alexithymia Scale-2 were applied. A stepwise multiple linear regression was calculated to predict alexithymia based on components of commitment and action, mindfulness facets, and demographic variables (F[5,578] = 77.26, p ≤ 0.001), with an R2 of 0.41. The predictive variables including description (B = -0.59, t = -8.02, p < 0.001), commitment and action (B = -0.13, t = -4.38, p < 0.001), observation (B = -0.15, t = -2.94, p < 0.01), and no judgment (B = -0.16, t = -2.56, p < 0.05) exhibited significant prediction effects on the adjusted index of alexithymia. The findings contribute to the potential mechanism between mindfulness and alexithymia in intervention that seeks to improve mindfulness and acceptance skills and could prove more effective in treating patients with 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 | 0.004 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".