Features and psychometric properties of the Montreal Cognitive Assessment: Review and proposal of a process-based approach version (MoCA-PA)
Bibliographic record
Abstract
The current study presents a rapid review of the psychometric features of the standard Montreal Cognitive Assessment (MoCA), and the proposal for a modified version of the test, informed by the methodology of the Boston Process Approach to neuropsychological assessment. In order to aid the process of identification of the primary underlying neurocognitive mechanism responsible for defective test performance, the MoCA-Process-Based Approach (MoCA-PA) adds complementary or satellite test conditions in some of its subtests, includes “new” qualitative indices to capture the cognitive processes involved in each cognitive task, and incorporates new qualitative classifications of error subtypes. It provides concurrent assessment of multiple cognitive processes within each task, without significantly increasing administration time or placing significant additional burden upon the respondent. We present preliminary results obtained from an initial sample of 45 community-dwelling older adults attending a University program for seniors. Results suggest the usefulness of additional indices in providing additional information on cognitive deterioration that may be overlooked with the only consideration of quantitative scores. Future research will aim to collect normative data for different clinical populations using the newly developed indices in order to determine the validity and clinical utility of the relatively novel qualitative process-based methods used in the MoCA-PA.
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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 | 0.030 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".