Exploring Metacognition in a Spanish-Speaking Population: Adaptation and Validation of the Metacognition Self-Assessment Scale (MSAS)
Bibliographic record
Abstract
Background/objectives: The study aimed to adapt and validate the Metacognition Self-Assessment Scale (MSAS) for Spanish-speaking populations. Metacognition, a multi-dimensional construct, holds a crucial role in understanding diverse psychological disordersand cognitive processes. Employing a modular approach to metacognition, the investigation focuses on specific sub- functions of metacognition such as self-monitoring, self-evaluation, and strategy selection. Method: A sample of 138 Spanish-speaking individuals partook in the study, which encompassed the translation of the MSAS and the execution of reliability and validity tests. Results: The results from confirmatory factor analysis support the originalfour-factor structure of the MSAS, including Self-Reflectivity, Critical Distance, Mastery of Coping Strategies, and Understanding Other Minds. Additionally, the study established convergent validity of the MSAS with the Toronto Alexithymia Scale (TAS-20), demonstrating a strong negative correlation between the two instruments. Conclusions: The successful adaptation and validation of the Spanish version of the MSAS holds substantial clinical implications for psychotherapeutic interventions and may provide insights into metacognitive processes for 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 | 0.003 | 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 | 0.000 |
| 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".