Quality of interactions in ECE settings and mean length of utterances among 4-year-old neglected children: Results from the ELLAN Study
Bibliographic record
Abstract
Language difficulties are frequently characterized by a significantly lower mean length of utterances (MLU) among children experiencing neglect. More opportunities to experience positive interactions, such as in early childhood education (ECE) settings, could help increase these children’s MLU. This study aims to examine the relationship between the quality of interactions within the group in ECE settings attended by children experiencing neglect and the presence of difficulties based on MLU (MLU-Ds). Eighteen (18) neglected (age = 48.26 months, standard deviation [ SD] = 0.37) and 86 non-neglected children (age = 48.07 months, SD = 0.24) participated in this study. To estimate the prevalence of difficulties, the MLU of all the participants was measured using a language sample. The Classroom Assessment Scoring System Pre-K was used to measure the quality of interactions in ECE settings attended by children experiencing neglect. Behavior Management ( p = .0072, adjusted R 2 = .47) and Concept Development ( p = .019, adjusted R 2 = .15) are associated with the MLU of neglected children presenting MLU-Ds. Although not statistically significant, the results obtained for the dimension of Regard for Child Perspectives ( p = .090, adjusted R 2 = .12) raise relevant trends to examine. This study highlights specific dimensions of quality of interactions that are associated with language skills of children experiencing neglect. It also supports the need to continue studies to have a more comprehensive portrait of this association.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 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".