A qualitative study of university students’ perspectives of hope during the COVID-19 pandemic
Bibliographic record
Abstract
Background: Students in higher education commonly experience mental health problems. There is an ongoing need to explore potential intervention targets to focus on mental health promotion among students. Hopefulness may alleviate or be protective against various negative mental health conditions such as depression, anxiety, suicide, and trauma-related disorders. Objective: To explore postsecondary students' meanings and experiences of hope during the COVID-19 pandemic and identify factors affecting hopefulness during crises. Methods: Purposive sampling was used to recruit participants for online semi-structured interviews in a university located in Southwestern Ontario, Canada. Data were analyzed using thematic analysis. Results: In total, 12 participants were interviewed, and 4 themes were generated: (1) hope is a complex concept with an associated set of behaviors, (2) cognitive framing of hope as a means of student resilience, (3) COVID-19 as an antagonist which amplifies preexisting student concerns and issues, and (4) the social and physical environments serve as barriers and enablers to hope and well-being. Hope was perceived as a positive mental trait, external events and the environment were reported to impact hope, and those who were generally more hopeful adjusted better mentally when unexpected circumstances arose. Conclusions: Findings shed light on the interconnectedness and complex nature of hope, its sources, and enablers. Novel findings include the ways in which hope was affected during the COVID-19 pandemic. Recommendations for individual- and community-based interventions include targeting enablers to hopefulness by promoting social support systems, offering virtual extracurricular activities, and delivering alternative approaches to teaching and learning.
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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 | 0.014 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.012 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".