101 The Impact of Sociodemographic Factors on Youth Academic Achievement During the COVID-19 Pandemic in Ontario, Canada
Bibliographic record
Abstract
Abstract Background The COVID-19 pandemic interrupted education and widened socioeconomic disparities. In Ontario, schools were closed for 28 weeks, longer than any other state, province, or territory in North America. School interruptions and remote learning are known to have short and long-term negative effects, which differ by socioeconomic status, on students’ academic outcomes. Objectives The objective of this study was to determine whether the COVID-19 pandemic was associated with a greater achievement gap between disadvantaged and non-disadvantaged secondary students in Ontario, Canada. Design/Methods We conducted a population-based observational study comparing scores from Ontario’s Education Quality and Accountability Office (EQAO) standardized Grade 9 Assessment of Mathematics from 2018-19 (pre-COVID year) and 2020-21 (COVID year). Our outcome measure was the EQAO Grade 9 Assessment of Mathematics dot scores. The primary analysis used a mixed-effects multilevel model (with random effects for school board) to calculate the interaction between year and neighborhood family income on math scores (difference in income slope between years) while controlling for confounders. Secondary analyses examined other risk factors (e.g. parental education, newcomer status, and lone-parent home) and their impact during the pandemic. Results In the COVID year, 3,485 of 42,920 eligible Ontario Grade 9 students (8.1%) participated in the EQAO Grade 9 Assessment of Mathematics from 38 of 72 school boards. In the pre-COVID year, within those same 38 school boards, 42,640 of 43,230 eligible Grade 9 students (98.6%) participated in the math assessment. We found that in the pre-COVID year, every one standard deviation increase (or decrease) in mean log-adjusted neighborhood income resulted in a mean increase (or decrease) in test score of 0.088 (95% CI, 0.078 to 0.097; p<0.0001). The effect of income on test score decreased in the COVID year (delta between years, -0.047, 95% CI, -0.060 to -0.033; p<0.0001). Conclusion This population-based observational study demonstrated that neighborhood family income impacted academic performance less in the COVID year compared to the pre-COVID year. The impact of other sociodemographic factors on academic achievement were also modified in the COVID year compared to pre-COVID. Determining how sociodemographic risk factors affected academic achievement during the pandemic may help inform educational recovery strategies and improve educational outcomes for children facing social inequities.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 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".