The Impact of Socioeconomic Disadvantages on Academic Achievement During COVID-19 School Disruptions
Bibliographic record
Abstract
The COVID-19 pandemic disrupted education worldwide, magnifying existing socioeconomic disadvantages. This narrative review utilized the SANRA—a scale for the quality assessment of narrative review articles to examine 20 articles from the past six years to explore the impacts of the shift to online learning, especially for children from lower-income households. Students from disadvantaged backgrounds faced significant challenges, leading to widening academic gaps. Key factors included limited access to technology and reliable internet, inadequate parental support due to work commitments or limited education, and emotional stress from social isolation, financial instability, and health concerns. While wealthier families could afford private tutors or extra resources, lower-income students struggled to maintain educational continuity. Additionally, parents with higher levels of education were better equipped to support their children’s learning. The digital divide left many students unable to fully participate in virtual classes or complete assignments, resulting in long-term learning losses. Food insecurity and unstable housing further hindered their focus on education. Addressing these disparities requires systemic interventions such as increased access to technology, targeted academic recovery programs, and stronger school-community partnerships. Without such measures, the educational inequities deepened by COVID-19 may persist for generations.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".