Parenting Practices, Technology Use, and Preschoolers' Self-Regulation During COVID-19: A Thematic Analysis
Bibliographic record
Abstract
The COVID-19 pandemic has caused unprecedented challenges and as a result, the health behaviours and stress levels of Ontarian families have been negatively impacted. The purpose of this study was to explore preschoolers’ self-regulation, parenting stress, and technology use in Ontario within the context of the COVID-19 pandemic. Participants included 11 parents of preschool-aged children who participated in interviews for the Children’s Technology and Media Use During the COVID-19 Pandemic study. Five themes were generated regarding parenting stress: stress related to their added role as teachers, stress related to their parenting role, cancelled and missed events, isolation, and lack of support. In detailing their child’s engagement in technology, three themes were generated: increased screen time, focusing on technology, and difficulties in emotion regulation. As a result of their parenting stress, three subthemes were generated in their parenting behaviours: using technology as a parenting tool, engaging in reactive parenting, and implementing rules for technology use. This novel study provides insight into the self-regulation of young children and how technology use and parenting stress have impacted this skill in young children living in Ontario during the COVID-19 pandemic. The results highlight the specific concerns parents have during the pandemic and the ways their children have been impacted by restrictions and increased technology use.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".