Student resilience to COVID-19-related school disruptions: The value of historic school engagement
Bibliographic record
Abstract
Does historic school engagement buffer the threats of disrupted schooling – such as those associated with the widespread COVID-19-related school closures – to school engagement equally for female and male high school students? This article responds to that pressing question. To do so, it reports a study that was conducted in 2018 and 2020 with the same sample of South African students ( n = 172; 66.30% female; average age in 2020: 18.13). A moderated moderation model of the 2018 and 2020 data showed that historic levels of school engagement buffered the negative effects of disrupted schooling on subsequent school engagement ( R² = .43, β = −5.09, p < .05). This protective effect was significant for girl students at moderate and high levels of historic school engagement, but not at lower levels of historic school engagement. Disrupted schooling did not significantly affect school engagement for male students at any level of historic school engagement. In addition, student perceptions of teacher kindness were associated with higher school engagement and having experienced an adverse event at school with lower school engagement. The results point to the importance of facilitating school engagement and enabling school environments – also when schooling is disrupted.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".