Does Full-Day Kindergarten Reduce Parenting Daily Hassles?
Bibliographic record
Abstract
Parenting daily hassles are viewed as the recurring demands associated with raising a young child (Crnic & Greenberg, 1990) and contribute to parental well-being and parenting relationships (Crnic, Gaze, & Hoffman, 2005). The goal of this study was to examine differences in the daily hassles reported by parents of half-day and full-day kindergarten students following the phased-in implementation of full-day kindergarten in Ontario. Based on results from a previous demonstration project of integrated kindergarten and childcare, it was hypothesized that parents of full-day kindergarten children would experience fewer daily hassles related to education and childcare. Four hundred and forty-nine parents participating in a longitudinal study tracking Ontario’s transition from half-day to full-day kindergarten were asked to complete a survey of their experiences with early childhood parenting daily hassles, as well as ademographic questionnaire as part of the larger study. We investigated whether parents of children enrolled in full-day programs experienced reductions in parenting daily hassles as compared to parents of children in half-day programs. Overall, parents of children enrolled in full-day kindergarten reported similar levels of daily hassles to parents of children in half-day programs. Additional analyses of demographic factors indicated that full-day kindergarten was related to lower levels of daily hassles for parents who worked full-time. Policy implications regarding integrated full-day kindergarten and childcare are discussed.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".