Remote learning – the cause of an increase in health symptoms in students during the COVID-19 pandemic
Bibliographic record
Abstract
Introduction:The multitude of changes related to remote learning during a pandemic pose a risk of increased effects on the psychophysical health of students.The main objective was to assess the increase in health symptoms in students during a pandemic and the association between the increase in these symptoms and problems with remote learning.Material and methods: Analyses were conducted on a sample of 340 parents (92% were mothers) of secondgrade students (51% girls) attending 11 primary schools.Logistic regression analysis was used to estimate the risk of increased health symptoms in the context of the difficulties experienced with remote learning.Results: During the pandemic almost 2/3 of students had an increase in irritability (64%) and outbursts of anger (65%).In nearly half of the students (43%) their parents noticed an increase in headaches, and in every third child (38%) increased abdominal pain.Children who had difficulties with remote learning were statistically more likely to experience increased sleep problems (34.3% vs. 11.4%,p = 0.001), decreased appetite (35.5% vs. 15.2%,p = 0.001), and increased abdominal pain (47.9% vs. 32.9%,p = 0.001) as well as all analysed emotional symptoms: increased fear/anxiety (44.8% vs. 25.4%,p = 0.007), sadness/apathy (56.5% vs. 38.0%,p = 0.003), irritability (70.0% vs. 49.3%,p = 0.009), and outbursts of anger (71.0% vs. 53.2%,p = 0.008).Difficulties with remote education increased the risk of sleeping problems [OR = 4.54; CI (OR): 2.05-10.04;p < 0.001], the risk of abdominal pain [OR = 3.45; CI (OR): 1.81-6.60;p < 0.001], and risk of the decreased appetite [OR = 3.039; CI (OR): 1.49-6.19;p = 0.002].For increased symptoms of headache, fear/anxiety, sadness/apathy, irritability, and outbursts of anger, the risk was 2-fold greater, and nearly double while students had remote learning problems.Conclusions: Problems with remote learning increased in children, primarily risk of physical health problems such as sleep problems, increased abdominal pain, and decreased appetite.Students experiencing distance learning difficulties and their parents are important beneficiaries of prevention and intervention programs.
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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 | 0.001 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".