MétaCan
Menu
Back to cohort
Record W4408938064 · doi:10.5944/rdp.v36i130.42041

Exploring Metacognition in a Spanish-Speaking Population: Adaptation and Validation of the Metacognition Self-Assessment Scale (MSAS)

2025· article· en· W4408938064 on OpenAlexaboutno aff
Miquel Alabèrnia-Segura, Danielle Mullins, Anna Carulla-Flix, Guillem Feixas

Bibliographic record

VenueRevista de Psicoterapia · 2025
Typearticle
Languageen
FieldPsychology
TopicInnovative Teaching and Learning Methods
Canadian institutionsnot available
Fundersnot available
KeywordsMetacognitionScale (ratio)PsychologyAdaptation (eye)PopulationCognitive psychologyCognitionGeographyMedicineCartographyPsychiatry

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.547
Threshold uncertainty score0.471

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.098
GPT teacher head0.376
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueRevista de PsicoterapiaSame topicInnovative Teaching and Learning MethodsFrench-language works237,207