Exploring International Graduate Nursing Students' Experiences With, and Response to, the COVID-19 Pandemic and Public Health Measures in Canada: An Interpretive Descriptive Study.
Bibliographic record
Abstract
Background: The COVID-19 pandemic and public health measures affected international graduate nursing students in diverse ways. The COVID-19 pandemic brought about the closure of educational institutions and the migration to remote teaching and learning. These and related actions limited access to resources such as office spaces and library services for students. In addition, many international students who worked on and off-campus lost their jobs, triggering financial crises. The lockdown and ban on international travel to protect the public from the COVID-19 virus meant that international students could not leave Canada to visit their loved ones abroad. These restrictions had a potential to affect the wellbeing of international students. \nAim: The aim of the study was to explore international graduate nursing students’ experiences with, and response to the COVID-19 pandemic and associated public health measures at a university in a Prairie Province. \nMethod: Interpretive Description (ID) was used to explore the experiences of eight international graduate nursing students in a nursing program located in a Prairie Province, Canada. Semi-structured individual interviews were conducted with participants after which the data were then transcribed and analyzed using six phases of thematic analysis. \nFindings: The study yielded three major themes: a) COVID-19’s Disruptions; b) Coping with COVID-19 Disruptions; and c) Finding Meaning and Growing amid COVID-19’s Disruptions. Participants reported financial difficulties due to the loss of financial support and jobs. International students who continued working in essential services such as long-term care facilities at the peak of the pandemic were exposed to the COVID-19 infections, however; the jobs provided financial stability for them. International students reported fear and worry about loved ones abroad, as some developed symptoms of mental health disorders as a result. \nConclusion: The COVID-19 pandemic affected international graduate nursing students’ wellbeing in various ways. Despite the impact on their wellbeing, they were resilient and continued their studies. They employed coping mechanisms such as talking to family members and friends via social media channels. Some also sought help from mental health specialists to mitigate difficult challenges of the COVID-19. Loss of community on campus may have been the most profound loss to the international graduate nursing students.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".