Developing a Computerized Brief Cognitive Screening Battery for Botswana: A Feasibility Study
Bibliographic record
Abstract
OBJECTIVE: To determine the feasibility of using a brief computerized battery for assessing cognition in citizens of Botswana. METHOD: A group of 134 healthy subjects were administered a brief computerized battery of tests (Stroop, Symbol Digit Modalities Test (c-SDMT), and 2 and 4 second versions of the Paced Visual Serial Addition Test (PVSAT)). Half the subjects were tested in English and the other half in Setswana. RESULTS: All subjects completed the 20 min battery. Participants administered the tests in English had more years of education (p < .001) and were more likely to be male (p = .024) than those administered the tests in Setswana. There were no significant cognitive differences between the English and Setswana groups. Predictors of cognition were education (c-SDMT, PVSAT 4 and 2 second versions), age (Stroop, c-SDMT), and sex (c-SDMT), with females performing better than males on the latter. Language was not associated with performance on any of the cognitive tests and there were no significant interactions between language and any of the demographic predictors of cognition. CONCLUSIONS: The results demonstrate that our brief computerized approach is feasible with similar findings obtained for both language groups. Two further phases to the development of the Botswana version of the brief computerized battery can now proceed. The first is to obtain normative data from a larger sample representative of Botswana society in general. The second will be to validate the cognitive measures in a sample of people with acquired brain injury using the normative data to determine thresholds for impairment.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".