Fear and stress related to COVID-19 and associated factors among undergraduate nursing students in Qatar
Bibliographic record
Abstract
BACKGROUND: The emergence of the coronavirus disease (COVID-19) has had an impact on nearly every human being with millions of related infections and deaths. The negative impact of the pandemic on individuals' mental health such as fear and stress, particularly among university students, have been reported. While the switch to online teaching and learning played an important mitigating role, it also had presented additional challenges to students' mental health. AIM: To examine the prevalence of fear and stress among undergraduate nursing students in Qatar and the factors associated with fear of COVID-19. METHOD: A cross-sectional design. An online survey was sent to students at the University of Calgary in Qatar. RESULTS: 135 participants completed the survey. The findings showed differences in fear of COVID-19 and stress and satisfaction with the measures proposed by the academic institution based on participants' demographic and COVID-19 profiles. Furthermore, fear of COVID-19 was associated with the age group (26-35), academic year level, and satisfaction with the measures proposed by the academic institution. CONCLUSION: The study found that switching teaching and learning online had a negative impact on participants' fear and stress. Several strategies were suggested to alleviate students' fear and stress and support them during future pandemics.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".