Montreal Cognitive Assessment (MoCA): A validation study among prisoners
Bibliographic record
Abstract
Abstract Background There are numerous scales for screening cognitive performance and thus identification of any potential deficits, but in spite of the vulnerability of the prison population to such problems, there has been no adequate validation of screening tools specifically for use with prisoners or others in the criminal justice system. Aim To validate the Montreal Cognitive Assessment (MoCA) for use with prisoners. Methods 100 adult prisoners in one Portuguese prison were randomly invited by clinicians to take part in this study. A same size sample of community‐living adult non‐offenders of similar age was selected from the MoCA's normative study database in Portugal. For both groups, the key inclusion criterion was fluency in the Portuguese language. All participants completed the Mini Mental State Examination (MMSE) and the MoCA, both in Portuguese translation. Cronbach's alpha coefficient was calculated as an index of internal consistency and Pearson's r correlations calculated. Group performances were compared using independent samples t‐test. Covariance analysis (ANCOVA) was computed with level of education as covariate. To measure the magnitude of the effect, was used. A receiver operating characteristics curve analysis was computed to evaluate the discriminatory accuracy of MoCA and MMSE. Results The MoCA showed a ‘reasonable’ internal consistency index (α = 0.75) as well as positive and significant correlations with the MMSE. As a cognitive measure, however, the MoCA showed consistently superior psychometric properties and higher discriminatory accuracy (MoCA = 89%) than the MMSE (65%). According to the Youden index, the optimal cut‐off point for the MoCA is below 24 points, whereas for the MMSE, it is below 27. Conclusions The MoCA is a valid cognitive screening tool for use with prisoners. Further validations against detailed cognitive evaluation would be a useful next step.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".