The Pragmatic Language Skills of Severely Neglected 42-Month-Old Children: Results of the ELLAN Study
Bibliographic record
Abstract
The goals of this study were twofold: (1) to compare the pragmatic language skills (i.e., social communication skills) of 42-month-old neglected children with those of same-aged non-neglected children and (2) to measure the prevalence of pragmatic difficulties among the neglected children. The study sample was composed of 45 neglected and 95 non-neglected 42-month-old French-speaking children. The Language Use Inventory: French (LUI-French) was completed with all parents. This measure, comprised of 159 scored items divided into 10 subscales, was used to assess the children’s pragmatic skills. The 10th percentile on the LUI-French (95% confidence interval ) was used to identify children with pragmatic difficulties. The neglected children had lower scores than the non-neglected children on all 10 dimensions of pragmatics evaluated ( p < .01), as well as lower LUI-French Total Scores ( p < .001). The effect sizes of these differences varied between 0.84 and 2.78. Forty-four percent of the neglected children presented significant pragmatic difficulties compared to 4.2% of their non-neglected peers ( p < .001). It can be concluded that exposure to neglect significantly compromises children’s pragmatic skills. These results support the need for interventions geared toward neglected children and their families to support the early development of their pragmatic skills.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".