Login / Signup

Removing the noose item from the Boston naming test: A step toward antiracist neuropsychological assessment.

Sarah K SaloJanice C MarceauxKarin J M McCoyRobin C Hilsabeck
Published in: The Clinical neuropsychologist (2021)
Objective: The Boston Naming Test-Second Edition (BNT-2), the "gold-standard" assessment of confrontation naming used to diagnosis disorders such as dementia, includes aculturally insensitive item, the noose. Given calls to stop structural racism in psychology, this study examined changes in scores and performance classification if the noose item were omitted from the BNT-2. Methods: Participants were 291 Black, White, and Latinx adults who were administered the BNT-2 within a comprehensive neuropsychological evaluation. Ethnoracial differences in BNT-2 scores with and without the noose item and percentages of participants answering the noose item incorrectly were investigated. Results: Significant differences were found between ethnoracial groups in BNT-2 raw scores, T-scores, and percentage of participants incorrectly answering the noose item. Follow-up analyses revealed White participants obtained significantly higher raw scores and had significantly fewer participants answer the noose item incorrectly than Black and Latinx groups, who did not differ significantly. For T-scores, Black participants obtained significantly higher scores than White participants who obtained significantly higher scores than Latinx participants. Despite these differences, giving credit for the omitted noose item changed performance classification for only 10 participants (3.4%). Conclusions: Performance classification did not change significantly for the vast majority of a large ethnoculturally diverse sample when giving credit for the noose item as if it were not administered. Therefore, the non-noose BNT-2remains accurate while reducing cultural insensitivity towards Black populations, emphasizing a step in working towards anti-racism and fostering culturally-competent services within psychology.
Keyphrases
  • psychometric properties
  • machine learning
  • deep learning
  • healthcare
  • primary care
  • mental health
  • high resolution