Wellness, blaming and coping during a pandemic: an analysis of perceptions on reddit
Bibliographic record
Abstract
Purpose This study aims to examine Reddit posts regarding the COVID-19 pandemic from a subreddit dedicated to the campus community of a large, research-intensive Canadian University. The goal was to determine what users were sharing regarding their mental health, well-being, problems, coping strategies and perceptions about the health measures taken to prevent further spread. Design/methodology/approach A total of 1,096 paragraphs were analyzed using the qualitative methodology of thematic analysis. Findings Many users expressed struggling with their mental health and well-being during the pandemic. Difficulties with online learning, finding paid study and affording the costs of living were also reported. Coping was largely conducted through online means and included sharing advice, emphasizing connectedness and communicating information. The mixed perceptions regarding health measures focused on responsibility and fairness, with many users blaming the university and public health units. Originality/value This study contributes to the evolving understanding of how different populations are affected by the COVID-19 pandemic in Canada, specifically, university students. Implications for providing assistance to university students during the current pandemic and future waves are also discussed.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".