Normative data for the Grooved Pegboard Test in Quebec-French middle-aged and older adults
Bibliographic record
Abstract
OBJECTIVE: The Grooved Pegboard Test (GPT), commonly used with middle-aged and older adults, assesses fine motor control, psychomotor speed, and visuomotor coordination. Previous studies have shown the potential influence of sociodemographic variables on performance for this test. The present study aims to develop normative data for the GPT in the middle-aged and elderly Quebec-French population. METHOD: The normative sample consisted of 266 individuals aged 50 to 90 years from the province of Quebec. Multiple regression analyses were performed to estimate the association between the predictors (age, sex, education, and handedness) and the performance on each trial. RESULTS: GPT performance was positively associated with female sex and negatively associated with age on all trials. Left-handedness was positively associated with performance on non-dominant hand trials. Finally, the interaction between age and education levels was positively associated with performance on the second non-dominant hand trial. Normative data are presented using regression equations. One should note that a gender imbalance was observed in the sample, limiting the generalizability of the normative data for men, especially in less-educated subgroups. CONCLUSION: This study will facilitate the identification of psychomotor impairments in the middle-aged and elderly Quebec-French population.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 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".