Analysis of the utility of the MoCA Test for the cognitive assessment of a university population in Cali.
Bibliographic record
Abstract
This research study focused on the evaluation of the Montreal Cognitive Assessment (MoCA) test applied to a population of university students from the Faculty of Psychology in the city of Cali. The main objective was to analyze the cognitive performance of these students using this screening tool and to detect possible trends or characteristics in their results. The Montreal Cognitive Assessment (MoCA) is a brief test designed to evaluate various cognitive functions, such as memory, orientation, language, concentration, executive function, and visuospatial skills. This test takes approximately 10 minutes and has a maximum score of 30 points, with a score of 26 or above considered within the normal range. The results showed an average performance. Specifically, 21.01% of university students had low levels in executive function and visuospatial skills, while 46.38% scored at a medium level, and 32.61% scored high. In the identification category, only two levels were observed: 3.67% at a medium level and 96.33% at a high level, indicating that no students scored low. In the attention and concentration category, 18.30% scored low, 42.48% scored at a medium level, and 39.22% scored high. In the language category, 5.81% scored low, 27.91% medium, and 66.28% high. Regarding the abstraction domain, 1.37%
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".