The Quebec Semantic Memory Battery: Development, Standardization, and Psychometric Assessment of a Semantic Memory Battery in French
Bibliographic record
Abstract
OBJECTIVE: People with aphasia often experience semantic memory (SM) impairment. To improve diagnostic outcomes, SM tasks should recruit various sensory input channels (oral, written, and pictographic), permitting accessible, complete evaluation. There is a need for SM batteries for French-speaking Quebecers that use multiple input channels. The present study, therefore, describes the development of a novel French-language semantic battery: la Batterie québécoise de la mémoire sémantique (BQMS), the assessment of the BQMS's psychometric properties, and the establishment of normative data for the BQMS. METHOD: We first developed eight SM tasks. Following a pilot validation study, we determined the BQMS's reliability and validity, to ensure consistent, accurate detection of SM impairment. Among French-speaking Quebecers with cerebrovascular aphasia (n = 10), people with the semantic variant of Primary Progressive Aphasia (n = 4), and healthy controls (n = 14), we examined its convergent validity, concurrent validity, test-retest reliability, and internal consistency. Finally, we established normative data for the BQMS by calculating cut-off scores per task that indicate SM impairment (in 93 cognitively healthy French-speaking Quebecers), stratified by sociodemographic variables associated with performance. RESULTS: The BQMS shows high concurrent, discriminant, and convergent validity, as well as good test-retest reliability and internal consistency. The cut-off score indicating SM impairment ranged from the 2nd to 25th percentiles (stratified by task, age, and sex). CONCLUSIONS: The BQMS's psychometric properties indicate that it could be a valuable clinical tool for detecting SM impairment. Our normative data will help clinicians detect such impairments.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".