Understanding the relationship between inattention and early literacy trajectories in kindergarten.
Bibliographic record
Abstract
The purpose of this study was to examine the relationship between inattention, academic enabling behaviors (i.e., motivation, engagement, and interpersonal skills), and early literacy outcomes. Kindergarten students (N = 181; 55.2% male; 62% white) from two research sites (Southeastern U.S. and Eastern Canada) were assessed using the Letter Naming and Letter Sound Fluency AIMSweb Tests of Early Literacy (Shinn & Shinn, 2012) at three points across the school year. Their teachers provided information on the level of attention-deficit/hyperactivity disorder symptoms (ADHD Symptom Checklist-4; Gadow & Sprafkin, 2008) and academic enabling behaviors (Academic Competence Evaluation Scales; DiPerna & Elliott, 2000). Structural equation modeling (SEM) was used to determine predictors of initial level and growth in early literacy. Specifically, a series of models were tested to determine if a multidimensional model of academic enablers (AEs) mediated the relationship. Engagement predicted students' initial levels of early literacy, suggesting that this is an important mediator to consider between inattention and early literacy skills. Motivation related positively to engagement. Inattention also predicted both motivation and interpersonal skills in the negative direction. These findings suggest that AEs play an important role in the relationship between inattention and early literacy. AEs provide malleable targets for intervention and should be considered when developing intervention for youth at risk for academic failure. (PsycINFO Database Record
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".