The Toronto Mindfulness Scale and the State Mindfulness Scale: psychometric properties of the Spanish versions
Bibliographic record
Abstract
Objectives: The Toronto Mindfulness Scale (TMS) and the State Mindfulness Scale (SMS) are two relevant self-report measures of state mindfulness. The purpose of this study was to examine the internal structure and to offer evidence of the reliability and validity of the Spanish versions of the TMS and SMS. Methods: = 223), and measures of trait mindfulness, decentering, non-attachment, depression, anxiety, stress, positive and negative affect, self-criticism, and self-reassurance. The internal structure of the TMS and SMS was analyzed through confirmatory factor analysis. Reliability, construct validity, and sensitivity to change analyses were performed. Results: The correlated two-factor structure (curiosity and decentering) was the best-fitting model for the TMS (CFI = 0.932; TLI = 0.913; RMSEA = 0.100 [0.077-0.123]; WRMR = 0.908). The bifactor structure (general factor, mindfulness of body, and mindfulness of mind) was the best-fitting model for the SMS (CFI = 0.961; TLI = 0.950; RMSEA = 0.096 [0.086-0.106]; WRMR = 0.993). Adequate reliability was found for both measures. The reliability of the SMS specific factors was very poor when controlling for the general factor. The patterns of correlations were mainly as expected and according to previous literature. The TMS and SMS have been able to detect state mindfulness changes after different meditation practices. Conclusion: Validity evidence is provided to support the use of the TMS and SMS in Spanish populations, though the reliability of the SMS specific factors merit revision.
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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 | 0.007 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".