Exploring academic integrity and mental health during COVID-19: Rapid review
Bibliographic record
Abstract
Purpose: The goal of this study was to understand the relationship between academic integrity and students’ mental health during the COVID-19 crisis. Methods: We employed a rapid review method to identify relevant data sources using our university library search tool, which offers access to 1026 individual databases. We searched for sources relating to the concepts of (a) COVID-19 crisis; (b) academic integrity; and (c) mental health. We delimited our search to sources published between 01 January and 15 May 2020. Results: Our search resulted in a preliminary data set of sources (N=60). Further screening resulted in a total nine (n=9) sources, which were reviewed in detail. Data showed an amplification of students’ anxiety and stress during the pandemic, especially for matters relating to academic integrity. E-proctoring of examinations emerged as point of particular concern, as there were early indications in the literature that such services have proliferated rapidly during the crisis, with little known about the possible impact of electronic remote proctoring on students’ well-being. Implications: Recommendations are made for further research to better understand the impact of e-proctoring of remote examinations on students’ mental health, as well as the connections between academic integrity and student well-being in general. Keywords: Academic integrity, mental health, rapid review, COVID-19, e-proctoring
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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 | 0.045 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.042 | 0.038 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.009 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".