Exploring parent–child interactions during a parent-implemented language intervention for children who are late-to-talk
Bibliographic record
Abstract
Supportive parent–child interactions are critical for facilitating typically developing children's language and social skill development. For children who are late-to-talk, parent–child interactions may be particularly important to address as a means of supporting growth in children's early language abilities. Target Word is one parent-implemented intervention for children who are late-to-talk that teaches parents strategies for facilitating more meaningful communicative interactions with their children, but little is known about the impact of this program on parent–child interactions. The aim of the present study was to determine whether the nature of parent–child interactions changed during the virtual Target Word program and whether changes in parent–child interaction were associated with parents’ participation in the virtual Target Word program. A total of 40 children between 16 and 27 months of age who were identified as late-to-talk and their caregivers were assigned to a treatment or wait-list control group. Parent–child interactions were captured virtually using Zoom at four time points and measured using the Parenting Interactions with Children: Checklist of Observations Linked to Outcomes (PICCOLO). Families completed book reading, pretend, and manipulative play tasks. While 16 of the 20 families in the treatment group completed the study, only 5 of the 20 families in the wait-list control group completed the entire study. Data were analyzed using a constrained longitudinal data analysis approach. Group differences in parent–child interactions were not statistically different at any time point. Further research is necessary to evaluate whether parent–child interactions are in fact impaired in late talkers, the psychometric properties of the PICCOLO for late talkers, and whether parent–child interactions may be assessed differently in virtual versus in-person environments.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".