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Record W3116099099 · doi:10.1024/1421-0185/a000242

The Psychometric Properties of the Montreal Cognitive Assessment (MoCA)

2020· article· en· W3116099099 on OpenAlexaboutno aff
Giovanni Sala, Hiroki Inagaki, Yoshiko Ishioka, Yukie Masui, Takeshi Nakagawa, Tatsuro Ishizaki, Yasumichi Arai, Kazunori Ikebe, Kei Kamide, Yoichi Gondo

Bibliographic record

VenueSwiss Journal of Psychology · 2020
Typearticle
Languageen
FieldMedicine
TopicDementia and Cognitive Impairment Research
Canadian institutionsnot available
FundersJapan Society for the Promotion of Science
KeywordsMontreal Cognitive AssessmentPsychologyCognitionSocioeconomic statusDevelopmental psychologyTest (biology)GerontologyCognitive impairmentDemographyPopulationMedicinePsychiatry

Abstract

fetched live from OpenAlex

Abstract. The Montreal Cognitive Assessment (MoCA) is a test assessing global cognition in older adults which is often used by researchers and clinicians worldwide, although some of its psychometric properties have yet to be established. We focus on three fundamental aspects: the factorial structure of the MoCA, its general factor saturation, and the measurement invariance of the test. We administered the MoCA to a large sample of Japanese older adults clustered in three cohorts (69–71-year-olds, 79–81-year-olds, and 89–91-year-olds; N = 2,408). Our results show that the test has an overall stable hierarchical factorial structure with a general factor at its apex and satisfactory general-factor saturation. We also found measurement invariance across participants of different ages, educational levels, economic status, and sex. This comprehensive investigation thus supports the idea that the MoCA is a valid tool to assess global cognition in older adults of different socioeconomic status and age ranges.

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)0.001
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 score0.369
Threshold uncertainty score0.239

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.062
GPT teacher head0.393
Teacher spread0.331 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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