Psychometric Properties of the Montreal Cognitive Assessment (MoCA) to Detect Major Neurocognitive Disorder Among Older People in Ethiopia: A Validation Study
Bibliographic record
Abstract
Background: Cognition is one of the most complex functions of the human brain, and major neurocognitive disorders affect this function causing a wide array of problems in an individual's life. Screening for major neurocognitive disorders can be helpful in designing and implementing early interventions. Purpose: This study was designed to assess the reliability and validity of the Montreal Cognitive Assessment (MoCA) tool to detect major neurocognitive disorders among older people in Ethiopia. Methods: One hundred and sixteen randomly selected older adults in Ethiopia were involved in a cross-sectional study. The Diagnostic and Statistical Manual of Mental Disorders criteria for major neurocognitive disorders was used as a gold standard. Data were analyzed using STATA v16 statistical software. Receiver operating curve analysis was performed, and inter-rater, internal consistency reliabilities, content, criterion and construct validities were determined. Statistically significance was declared at a p-value of <0.05. Results: The study had a 100% response rate. The mean age of the study participants was 69.87 ± 7.8. The inter-rater reliability value was 0.96, and Cronbach's alpha was 0.79. The optimal cutoff value was ≤21, and Montreal Cognitive Assessment has an area under curve value of 0.89. The sensitivity, specificity, positive and negative likelihood ratios, and positive and negative predictive values of MoCA are 87.18%, 74.03%, 3.35, 0.17, 63%, and 91.9%, respectively. The tool also has good concurrent and construct validities. Conclusion: The Montreal Cognitive Assessment tool was a reliable and valid tool to detect major neurocognitive disorder. It can be incorporated into the clinical and research practices in developing countries.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".