Quality of interactions offered to infants, toddlers and preschoolers in home-based childcares: an exploratory study
Bibliographic record
Abstract
This study explores the variation in quality of interactions offered to infants, toddlers and preschoolers in home-based childcare (HBC). While high quality interactions promote children's development, the quality levels offered to 0–3-year-olds appear lower then those offered to groups of children aged 3-5 years (Lahiti, 2015; Vermeer, 2016). Yet, little is known about the nature of quality of interactions in HBC, a multiage context (Ang et al., 2016) . Quality of interactions is conceptualized with the Teaching through framework (Hamre et al., 2013) about important interactions for children’s development and learning. This quantitative study concerns 10 HBC in Montreal (Quebec, Canada). Interactions in each childcare were videotaped for 3 hours during a single visit and were later scored using the CLASS tool. Observers completed 6 observation cycles, alternating between versions of the tool - Infant, Toddler, PreK. All providers and the children’s parents were informed about the project and signed a consent form. T-test showed significantly lower levels of quality on the CLASS Infant as compared with the CLASS Pre-K (t(9)= -3.93, p = .003), as well as \nsignificantly lower levels of quality on the CLASS Toddler as compared with the CLASS Pre-K (t(9)= -3.68, p = .005). The scores of the CLASS Infant and Toddler were not significantly different. Providers' training should address even more interactions that support infant and toddler’ development, the multiage context, as well as dimensions related to educational support. Quality monitoring \nsystems should target quality of interactions offer to infants and toddlers, especially in HBC.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".