Developing an automated language-based cognitive assessment; CognoSpeak – working with an under-represented ethnic minority group
Bibliographic record
Abstract
Background Current memory screening tools were developed based on normative data from almost exclusively white populations. This can unfairly disadvantage access to diagnoses for BAMER communities, and individuals who use English as an additional language. We collaborated with Israac, a Sheffield-based Somali community group, to investigate how Somali and white British individuals’ responses differ on both traditional cognitive screening tools and in our automated cognitive assessment tool, CognoSpeak™ . Methods We trained two research champions from Israac to recruit and assess Sheffield- based Somali participants using the Montreal Cognitive Assessment (MoCA), Rowland Universal Dementia Assessment Scale (RUDAS), Multicultural Cognitive Examination (MCE), and CognoSpeakTM. Results Preliminary analysis of 36 Somali and British-Somali participants found that 47.2% of participants scored below the MoCA threshold of 26/30. In contrast, only 5.56% of the participants scored below the MCE threshold of 70/100, and no participants scored below the RUDAS threshold of 23/30. Conclusion Cognitive tools must be co-developed for cross-cultural use. Training individuals from within the community to conduct research improved recruitment and community trust in researchers. We will present a larger data set from both the Somali and white British populations results’ on CognoSpeak and the three traditional cognitive assessments.
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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 | 0.012 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".