Mini-SEA: Validity and Normative Data for the French-Quebec Population Aged 50 Years and Above
Bibliographic record
Abstract
OBJECTIVE: The mini Social cognition & Emotional Assessment (mini-SEA) is a social cognition battery which assesses theory of mind and emotion recognition. Currently, no psychometrically validated measure of social cognition with adapted normative data exists for the middle-aged and elderly French-Quebec population. This project aims to determine the known-group discriminant validity of a cultural and linguistic adaptation of the mini-SEA between cognitively healthy people, those with mild cognitive impairment (MCI) or living with Alzheimer's Disease (AD). This study also aims to examine the stability of mini-SEA's performance over a 3-4-month time period, as well as to produce normative data for French-Quebec people aged 50 years. Normative data are derived for the full and an abbreviated version of the Faux Pas subtest. METHOD: The sample included 211 French-speaking participants from Quebec (Canada) aged 50 to 89 years. Mini-SEA's performance between a sub-sample of cognitively healthy people (n = 20), those with MCI (n = 20) or with AD (n = 20) was compared. A sub-sample of cognitively healthy people (n = 30) performed the task twice to estimate test-retest reliability. Socio-demographic variables' effects on scores were examined to produce normative data in the form of regression equations or percentile ranks. RESULTS: Significant differences emerged between cognitively healthy people and those with MCI or AD. Moreover, scores were relatively stable over a period of 3 to 4 months. Finally, for the normative data, age, gender, and education were associated with performance on the mini-SEA or its subtests. CONCLUSIONS: This study improves and standardizes social cognition's assessment among French-Quebec individuals, which will help characterize their cognitive profile.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".