Cumulative Family Sociodemographic Risk and Preschoolers’ Language Skills Development: The Mediating Role of the Home Literacy Environment
Bibliographic record
Abstract
Research Findings: This longitudinal study examined the mediating role of home literacy environment (HLE) in the relationship between family sociodemographic risk factors and preschoolers’ language skills during their transition to kindergarten. A sample of 142 children showing vulnerability regarding school readiness were assessed three times. Eight months before kindergarten, family sociodemographic information was collected, and a cumulative family sociodemographic risk score was calculated. Children’s language skills (i.e. receptive and expressive vocabulary and sentence recall) were also collected at this timepoint. Four months later, three components of HLE (i.e. frequency of parents’ involvement in literacy activities, quality of parents’ involvement in literacy activities, and availability of learning materials at home) were measured. At the end of kindergarten, 1 year later, children’s language skills were reassessed. One main indirect association was found: cumulative risk was related to expressive vocabulary through the availability of learning materials. Practice or Policy: These findings highlight that interventions with parents of preschoolers showing vulnerability regarding school readiness should focus on supporting parental practices and helping parents gain access to learning materials through school, social services, and other community resources.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".