Elementary students’ social, emotional, and cognitive development during the COVID- 19 pandemic in North America: A scoping review
Bibliographic record
Abstract
The COVID- 19 pandemic and subsequent disruptions to in-person learning presented unanticipated challenges to elementary school systems across North America. Elementary students are particularly vulnerable to the negative outcomes of extended online learning, as this developmental stage is crucial for the social, emotional, and cognitive growth of students. To date, no research has synthesized what we currently know about the impacts of the pandemic on these developmental outcomes among students. As such, this scoping review explores and synthesizes what is currently known regarding elementary students' social, emotional, and cognitive development during the COVID- 19 pandemic in North America. In accordance with the Joanna Briggs Guide, three databases were searched in September 2024, and results were uploaded into Covidence wherein they were screened for eligibility. A total of 13 peer-reviewed articles met the inclusion criteria for this review (i.e., included elementary students, spoke to social/emotional/cognitive development, peer-reviewed articles written in English and conducted in North America). Thematic analyses revealed four themes including (1) lack of socialization opportunities; (2) emotional regression; (3) cognitive gaps in learning; and (4) inequitable impacts on development. School-based supports are needed for all students, but particularly those facing multiple, intersecting health and social disparities, to bolster positive development post-pandemic and to mitigate long-term negative health outcomes.
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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 | 0.008 | 0.042 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".