Factor Structure and Psychometric Properties of the Learning, Executive, and Attention Functioning (LEAF) Scale in Young Adults
Bibliographic record
Abstract
The Learning, Executive, and Attention Functioning (LEAF) scale is a resource-friendly means of assessing executive functions (EFs) and related constructs (e.g., academic abilities) in children and adolescents that has been adapted for use with adults. However, no study in any population has investigated the factor structure of all LEAF EF items to determine whether items factor in a manner consistent with the originally proposed scale structure. Therefore, we examined LEAF scale responses of 546 young adults ( M age = 20.05, SD = 2.17). Upon removing academic items following a preliminary factor analysis, we performed principal axis factoring on the remaining 39 EF items. The final model accounted for 61.75% of the total variance in LEAF EF items and suggested that these items assess six moderately correlated EF constructs in young adults. We constructed six updated subscales to help researchers measure these EFs in young adults using the LEAF scale, each of which uniquely and differentially predicted measures of self-reported impulsivity, academic difficulties, and learning-related disorder history. Overall, the LEAF promises to be an accessible means of assessing a range of EF constructs in young adults, particularly when updated subscale structures based on factor analysis are used.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".