Normative Data for the Famous People Fluency Test in the Adult French-Quebec Population and Validation Study in Mild Cognitive Impairment and Alzheimer’s Disease
Bibliographic record
Abstract
OBJECTIVE: The production of words in verbal fluency tests relies heavily on executive functions and linguistic abilities. New tests such as the famous people fluency test can also be useful in clinical practice and research. This test, in which participants are asked to name so many famous people, has the potential to distinguish healthy individuals from participants with neurological disorders such as mild cognitive impairment or Alzheimer's disease. METHOD: The aim of this study was to determine the psychometric validity of the test (Study 1) and to provide normative data in the adult population of French Quebec for the famous people fluency test (Study 2). RESULTS: The results of the normative study, derived from a sample of 378 healthy individuals between the ages of 50 and 92, showed that age and educational level significantly influence performance on the test. Therefore, percentile ranks were calculated for performance on the famous people fluency test, stratified for these two variables. The results of Study 2 showed that the test differentiated the performance of healthy participants from the performance of participants with mild cognitive impairment or Alzheimer's disease. The results also showed that the famous people fluency test has adequate convergent validity, established with a semantic fluency test, and that the results showed good stability over time (test-retest validity). CONCLUSION: Norms and psychometric data for the famous people fluency test will improve the ability of clinicians and researchers to better recognize executive and language impairments associated with pathological conditions.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
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".