Cognitive and Emotional Well-being of Preschool Children Before and During the COVID-19 Pandemic: Evidence from a Canadian Birth Cohort
Bibliographic record
Abstract
The current study examines the association between pandemic exposure and child neurocognitive and socioemotional development at 24 months (N =718) and 54 months (N =703). Participants were recruited as part of the Ontario Birth Study (OBS), a prospective pregnancy cohort in Canada. Neurodevelopment was measured using the ASQ-3 and MCHAT-R at 24 months. The NIH Toolbox was used to assess neurocognitive and socioemotional function at 54 months. Analyses included logistic or linear regression with covariate adjustment. At 24 months, pandemic-exposed children had higher problem-solving and fine motor skills, but lower personal-social skills, compared to non-exposed children. At 54 months, pandemic-exposed children had significantly higher receptive vocabulary, visual memory, and overall cognitive performance compared to non-exposed children, with no differences found for socioemotional development. In this relatively advantaged and somewhat homogenous Canadian sample, evidence for both positive and negative associations between pandemic exposure and preschool children’s cognitive and emotional well-being were observed.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".