C - 05Use of the Montreal Cognitive Assessment (MoCA) in a Rural Outreach Program for Military Veterans
Bibliographic record
Abstract
Objective: The Montreal Cognitive Assessment (MoCA) is a free, easily accessible screener ideal for use in rural areas where resources are limited. We examined administration/scoring by non-clinical, trained staff; compared positive screening rates using two cut-off scores; and examined predictors of education-adjusted scores in N = 168 rural military Veterans from the Alabama Veteran Rural Health Initiative (VCOHW). Method: Participants recruited through the VCOHW and completed baseline assessment which included the MoCA. Assessments were administered by non-clinical VA employees trained on MoCA administration and scoring by experienced clinicians. Participants ranged in age from 21 to 85 with a mean age of 55.6 years; 91.3% were men and 7.1% were women. Self-identified race/ethnicity revealed: 58.6% White/Caucasian, 40.9% Black/African American, 1% Hispanic (n = 2), and 0.5% Asian (n = 1). 53% reported formal education past high school. Baseline measures were completed through self-report or interview for illness burden, occupational and functional disability, psychiatric symptoms, stress and trauma checklists, and healthcare utilization. General demographics, military history, and legal history were also included. Results: Accuracy of administration (95%) and scoring (68%) was calculated on audited MoCAs. Higher than expected rates of positive screens were observed (40% using 24/30 cutoff) in this relatively young (M = 55 years) community-dwelling sample. Correlation analyses revealed age was negatively correlated with overall performance (−.48, p < .001). Age was also negatively correlated with five of six cognitive domains: visuospatial/executive abilities, naming, delayed recall, orientation, and attention. Abstract thinking was not significantly related to age. An ANOVA predicting the total education-adjusted MoCA score from subjective health, race, age, and education revealed a significant model [F (4, 158) = 17.48, p < .0001], such that age (t = −7.63, p < .001), race (t = −2.51, p = .01), and education (t = 3.25, p = .001) significantly predicted MoCA scores. Subjective health was not significant. Conclusions: This study advances rural practice by being the first to: 1) examine MoCA scores in a rural, Deep South U.S. sample; and 2) report fidelity administration data for non-expert outreach staff.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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".