Cultural bias in the assessment of language: A closer look at the Boston naming test among multicultural Canadian older adults
Bibliographic record
Abstract
OBJECTIVE: The Boston Naming Test (BNT) is commonly used to assess word-finding in older adults but performance may be impacted by cultural and linguistic factors. This study aimed to assess cultural bias in BNT performance among older adults, explore sources of this bias and provide clinical guidelines for its use in multicultural settings. METHODS: We conducted a retrospective chart review of 525 older adults referred for neuropsychological assessment at a large geriatric hospital in a multicultural Canadian city. Participants were categorized by birthplace (Canada vs. outside Canada), and relationships between BNT scores, years in Canada, sociodevelopmental context of region of birth and first-language status were examined. RESULTS: Individuals born outside of Canada had significantly lower BNT scores than Canadian-born participants. These differences were not fully explained by English as a first language status or age at immigration though a significant correlation was observed between BNT scores and years in Canada. Sociodevelopmental context, measured by the Historical Index of Human Development (HIHD), partially mediated the relationship between region of birth and BNT performance. CONCLUSIONS: The BNT is influenced by cultural and linguistic factors, which may lead to inaccurate cognitive assessment in diverse populations. Clinicians should interpret BNT scores with caution in multicultural contexts and consider sociocultural factors to improve diagnostic accuracy and cultural sensitivity.
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".