Mental Health Problems among Elementary School Students Mandated to e-Learning: A COVID-19 Rapid Review Caveat
Bibliographic record
Abstract
Extended lockdowns during the COVID-19 pandemic mandated millions of students worldwide to e-learning and by default made many of their parents proxy homeschool teachers. Preliminary anecdotal, journalistic and qualitative evidence suggested that elementary school children and their parents were probably most vulnerable to this stressor and most likely to experience mental health problems because of it. We responded with a rapid review of 15 online surveys to estimate the magnitude of such risks and their predictors between 2020 and 2021. The pooled relative risk of mental health problems among school children and their parents was substantial (RR = 1.97). Moreover, this synthetic finding did not differ significantly between 10 child mental health outcomes (primarily measures of anxiety or depression) and five parental stress outcomes. Such risks to children and parents were incrementally greater among Latinx (RR = 1.81) and Black families (RR = 2.50) than among non-Hispanic White families (RR = 1.58) in the USA. Finally, such risks in the West (RR = 2.12) were observed to be greater than those in the East (RR = 1.36). Grave risks were experienced worldwide, but the pandemic once again clarified for the world that such structural violence, in this instance, in elementary school systems, was much more prevalent and virulent among Black and Brown families in places like the USA. Educational practice implications, future research and pandemic preparedness needs 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 | 0.022 | 0.124 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".