Language Difficulties Among Children Experiencing Neglect: A Public Health Approach Aimed at Narrowing the Gap
Bibliographic record
Abstract
Purpose Child neglect affects approximately 1% of children under the age of 6 years in the United States and Canada annually. Nearly 50% of children who experience neglect present significant language difficulties before starting school. Child neglect can thus be considered a major public health issue. Child neglect is an ecological and systemic phenomenon characterized by a dual disruption: first, in the relationship between the parent and child and second, in the relationship between the family and the wider community. Empirical research has quite convincingly demonstrated that parenting behaviors constitute a malleable variable upon which it is possible to act to foster the language development of young children. However, given the multidimensional nature of child neglect, interventions aimed directly at parents are simply not enough to support the language skills of children in this context. Based on a complex family and social conception of neglect, the objective of this article is to propose a logical model illustrating public health services for children experiencing neglect. Such a logical model provides for multiple interventions at the child, family and community levels. Conclusions Broader efforts should be made to support the parents in overcoming the challenges of parenthood by addressing the multiple risk factors to which they are exposed. This can be achieved both by striving toward positive environmental conditions, characterized by responsive caregiving in the home and community, and by implementing multilevel interventions within an interdisciplinary and intersectoral approach. The resulting recommendations align with the Individuals with Disabilities Education Act, a law that requires states to provide services to children in early intervention programs (Part C) and kindergartens/schools (Part B).
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".