MétaCan
Menu
← Back to cohort
Record W4390915955 · doi:10.7759/cureus.52351

Feasibility Study of the Boston Naming Test for the Arab Population

2024· article· en· W4390915955 on OpenAlexaboutno aff
Hadeel A Basura, Mohammed A Mudarris, Fatimah B Almubarak, Shahad A Alzahrani, Hajar Alghamdi, Ahlam I. Al‐Sulami, Ameerah Alnakhli, Ghadi Alzahrani, Haythum O. Tayeb

Bibliographic record

VenueCureus · 2024
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology of Language and Bilingualism
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineBoston Naming TestTest (biology)PopulationNormativeGerontologyClinical psychologyNeuropsychologyCognitionPsychiatry

Abstract

fetched live from OpenAlex

Introduction The Boston Naming Test (BNT) is a widely used US neuropsychological evaluation of confrontation naming for the examination of adults and children with learning disabilities and diagnosis of communication disorders, aphasia, dementia, and acquired brain injury or dysfunction. The purpose of the present study is to evaluate the practicality of the original English version of the 60-item BNT (BNT-60) on an Arab population and the need for a new adaptive Arabic version sensitive to cultural biases and to offer normative data that can serve as a reference for researchers and clinicians in the Gulf region, especially the Kingdom of Saudi Arabia (KSA). Data relating to the familiarity degree of the BNT-60 were also collected. Methods This research involved 105 randomly selected and cognitively healthy college students who were native Arabic speakers recruited in Jeddah. The Montreal Cognitive Assessment (MOCA) was administered with a cutoff score of 26. The participants were examined for naming accuracy, naming agreement, and familiarity in using the BNT-60. The data were then analyzed and compared with the findings from studies conducted in the United States. Results The BNT-60 was administered to 105 university students from the KSA, and the results were compared with the BNT-60 booklet norms (second edition). Their average performance was noticeably below the norms established by the original test standards. Compared with the participants in the US studies, the participants made approximately 65% more errors on the items including pretzel, wreath, beaver, harmonica, acorn, stilts, harp, hammock, knocker, pelican, muzzle, unicorn, funnel, accordion, asparagus, tripod, yoke, and trellis and 25% more errors on the items including seahorse, dart, igloo, sphinx, palette, and abacus. The item "boomerang" was not compared with the US sample because of differences in the version of the BNT, but the errors in naming this item were as frequent as those in naming the other misrecognized items. The internal consistency among the items' degrees of familiarity was also very high (α = 0.966), and a significant connection (r = 0.837, P < 0.001) was observed between object familiarity and naming accuracy. The Arabic-speaking population in the KSA and English-speaking population in the United States showed very different levels of familiarity with numerous items. Conclusion The participants' familiarity with the BNT objects varied depending on their culture and impacted their naming accuracy and overall scores on the test. Accordingly, the possibility of cultural biases should be considered when administering the BNT to the population of the KSA and the possibility of making changes so that the test better reflects the Arab culture as suggested.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.008
metaresearch head score (Gemma)0.018
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.018
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.072
GPT teacher head0.353
Teacher spread0.282 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

Explore more

Same venueCureus→Same topicNeurobiology of Language and Bilingualism→French-language works237,207→