The Grenada Learning and Memory Scale: Psychometric features and normative data in Caribbean preschool children
Bibliographic record
Abstract
OBJECTIVE: Neuropsychological assessment of preschool children is essential for early detection of delays and referral for intervention prior to school entry. This is especially pertinent in low- and middle-income countries (LMICs), which are disproportionately impacted by micronutrient deficiencies and teratogenic exposures. The Grenada Learning and Memory Scale (GLAMS) was created for use in limited resource settings and includes a shopping list and face-name association test. Here, we present psychometric and normative data for the GLAMS in a Grenadian preschool sample. METHODS: Typically developing children between 36 and 72 months of age, primarily English speaking, were recruited from public preschools in Grenada. Trained Early Childhood Assessors administered the GLAMS and NEPSY-II in schools, homes, and clinics. GLAMS score distributions, reliability, and convergent/divergent validity against NEPSY-II were evaluated. RESULTS: The sample consisted of 400 children (190 males, 210 females). GLAMS internal consistency, inter-rater agreement, and test-retest reliability were acceptable. Principal components analysis revealed two latent factors, aligned with expected verbal/visual memory constructs. A female advantage was observed in verbal memory. Moderate age effects were observed on list learning/recall and small age effects on face-name learning/recall. All GLAMS subtests were correlated with NEPSY-II Sentence Repetition, supporting convergent validity with a measure of verbal working memory. CONCLUSIONS: The GLAMS is a psychometrically sound measure of learning and memory in Grenadian preschool children. Further adaptation and scale-up to global LMICs are recommended.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".