244 Child Gender and Birth Order Influence Outcomes of an Early Intervention Program at Age 7 Years
Bibliographic record
Abstract
Background and aim Early intervention programs are critical to optimize development for children in low-income families. Principles of social justice and inclusion increase the tendency to employ similar early intervention approaches for all program children. This approach fails to maximize intervention outcomes, and may benefit certain sub-groups of children more than others. The purpose of this study was to explore differences in receptive language scores in children who participated in a two-generation preschool program while controlling for child characteristics. Method The program included centre-based care, parenting education, and family support. We assessed 62 children using the Peabody Picture Vocabulary Test III (PPVT-III) at program entry and exit, and age 7 years. Results Repeated measures ANOVA’s using child characteristics as covariates, revealed gender differences in receptive language scores at age 7 years favoring males, F(1, 61) = 3.71, p=0.06. Children with an older sibling exhibited significantly better receptive language scores, F(1.61) = 4.38, p=0.04. Ethnicity, English as a first language, time in program, and family income were unrelated to receptive language scores, p’s > 0.10. Conclusions The finding that males outperformed females is surprising because females tend to have stronger language skills than age-matched males. Younger siblings may have benefited from increased exposure to older siblings who had participated previously in the program. Results suggest that early intervention programs for children living in low-income families may benefit from alterations to program curricula that promote sex-differentiated learning strategies and focus on family dynamics.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".