Final Program Addendum 2020 Virtual Event International Neuropsychological Society July 1-2, 2020
Bibliographic record
Abstract
Taking the Test: Experiences of first-time neuropsychological testtakers in South Africa.Objective: Demand for neuropsychological (NP) testing with culturally/linguistically diverse individuals, including those with limited or no standardized NP testing experience and living in low-and middle-income countries (LMICs), is increasing.Because no research has examined the experience of undergoing NP testing in any population and how it could affect NP test performance, this qualitative study interviewed Xhosaspeaking South Africans about their first experience taking NP tests.Participants and Methods: A guided, semi-structured interview covering the causes of cognitive problems and five domains of the NP testing experience (patterns of abilities, cultural values, familiarity, language, and education) was used.Twenty-two (15 HIV+, 7 HIV-) South Africans (Age M (SD) -33.50 (5.88); % Male-32%) were interviewed.Interviews underwent text review and thematic analysis.Results: Eleven interviewees (50%) thought NP testing was to "check" or "look into" the brain; six (27%) thought it was to assess specific abilities (e.g.memory).Ten (45%) reported feeling nervous due to uncertainty about testing or appearing "dumb".Eleven (50%) and fifteen (68%) described being unbothered by stopping mid-task and not receiving feedback on performance, respectively.Seven (32%) reported performing tasks quickly and accurately to be contradictory.Among causes of cognitive problems, stress (n=5, 23%), excessive worry (n=4, 18%), old age (n=2, 9%), depression (n=1, 5%), and witchcraft (n=1, 5%) were the most commonly reported.Conclusions: There may be limited understanding of the purpose of NP testing, as well as anxiety provoking situations during testing in this sample.Furthermore, there may be limited knowledge of what causes cognitive problems.In LMICs, such as South Africa, neuropsychologists should evaluate an examinee's understanding of NP testing to help foster optimal testing conditions, and may need to provide education on the causes of cognitive problems.
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 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 | 0.002 |
| 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 | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.709 | 0.432 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".