Resilience and Despair
Bibliographic record
Abstract
The purpose of the current study was to explore graduate students’ mental health and educational experiences during the COVID-19 pandemic. Graduate students (N = 28) in Canada completed an online survey consisting of both closed- and open-ended questions related to their mental health, degree progress, and access to campus workspace. Data were analyzed using both quantitative and qualitative approaches before being synthesized through a pillar integration joint display to merge study findings. Based on self-report data, approximately 60% of participants were experiencing poor-to-moderate mental health at the time of the survey. Participants also expressed dissatisfaction with online learning and felt uncertain about their degree trajectory due to changes and restrictions associated with the pandemic. Based on the participants’ responses, recommendations for assisting graduate students during the pandemic are presented. Highlighted by these recommendations is the importance of accessing workspace on campus and the challenges associated with university mental health resources. Overall, nearly 16 months into the pandemic, participants’ mental health was negatively impacted by the restrictions. Although the study findings may not be generalizable to all post-secondary institutions, they can be used to inform university administrators regarding the continued challenges facing graduate students during the pandemic.
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 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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".