Impact of the COVID-19 pandemic on the mental health and learning of college and university students: a protocol of systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: COVID-19 has a serious impact on people's physical health and mental health. The COVID-19 pandemic forced college and university students to take online classes, which may have bad impacts on students' learning. In addition, the students lost many job opportunities during the pandemic. Faced with employment and study pressure and worried about the epidemic, college and university students were prone to increased overall negative emotion, anxiety and depression. Therefore, this systematic review and meta-analysis will be conducted to explore the impact of the COVID-19 pandemic on the mental health and learning of college and university students. METHODS AND ANALYSIS: We will conduct electronic literature search of the PubMed, Embase, Cochrane Library, Web of Science and Chinese National Knowledge Infrastructure databases. Two researchers will independently screen the studies, extract data and assess the quality of the included studies. Any disagreement will be resolved by the third investigator. The Newcastle-Ottawa Scale and other tools will be used to assess the risk of bias, according to the study design of included studies. OR, risk ratio, mean difference and 95% CI will be considered as the effect size. Heterogeneity between studies will be assessed by subgroup and sensitivity analysis, and publication bias will be detected by funnel plots, Begg's test and Egger's test. ETHICS AND DISSEMINATION: This systematic review and meta-analysis involves no patient contact and no interaction with healthcare providers or systems. We will disseminate the findings of this study through the presentation at scientific conferences and publication in a peer-reviewed journal. PROSPERO REGISTRATION NUMBER: CRD42020201132.
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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 | 0.099 | 0.128 |
| Meta-epidemiology (narrow) | 0.006 | 0.005 |
| Meta-epidemiology (broad) | 0.027 | 0.038 |
| Bibliometrics | 0.015 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.009 | 0.005 |
| Insufficient payload (model declined to judge) | 0.036 | 0.004 |
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".