COVID-19 Fatigue and Middle School Students’ Engagement and School Aversion: The Mediational Roles of Emotion Regulation and Perceptions of School Climate
Bibliographic record
Abstract
Learning during the COVID-19 pandemic has included disruption, uncertainty, and additional stress for students. Adverse learning outcomes are a growing concern, especially for vulnerable groups such as middle school students. While COVID-19 research is currently emerging, more research needs to address the specific experiences of middle school students. The current study examined the meditating role of coping (emotion regulation strategies) and perceptions of school climate on the relationship between COVID-19 fatigue and student outcomes (student engagement and school aversion) in a sample of middle school students (N = 301). Findings from parallel mediation path models indicated that COVID-19 fatigue was inversely related to student engagement and positively related to school aversion. School climate provided a moderate to strong mediation, and emotion regulation provided small partial mediation compared to school climate. The findings suggest that utilizing adaptive emotion regulation strategies can help promote student engagement and dampen school aversion in relation to COVID-19 fatigue. Additionally, positive perceptions of school climate can encourage school engagement and reduce school aversion. A deeper explanation of the importance of regulation and the way middle schoolers perceive school rules and support in the context of the COVID-19 pandemic is 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 | 0.001 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 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".