Clinical experiences of final-year RN and BSc nursing students during the first wave of the COVID-19 pandemic in Guyana: A mixed-methods study
Bibliographic record
Abstract
Objective: The study aimed to explore the clinical experiences of final-year RN and BSc nursing students from four selected nursing schools in Guyana during the first wave of the COVID-19 pandemic.Methods: The study utilized a mixed-methods approach. The sampling involved final-year RN and BSc nursing students. The quantitative design included a survey with a convenient sample of 157 students, and the qualitative approach included seven focus group interviews. The survey data were analyzed using inferential statistics and thematic analysis.Results: The findings revealed the psychological and structural challenges experienced by final-year RN and BSc nursing students during the early months of the pandemic in Guyana. Of the respondents, 82.8% indicated fear of contracting coronavirus, 58% felt depressed, and 56.7% were insecure about daily nursing duties. Structural challenges experienced were that 58% needed PPE readily available, 80.9% felt unsafe in the clinical areas, 71.3% did not receive COVID-19 training, and 70.7% experienced financial burden. Positive responses included 91.7% did not take leave intentionally, and 74.5% felt confident. Thematic analysis yielded categories on stigmatization, fear of contracting, mental stress, shortage of staff and PPE, absence of risk allowance, and disaster planning. Coping strategies indicated were supporting one another, sharing information and resources, and praying, which enabled one to navigate the challenges and highlighted the strength of resilience and teamwork.Conclusions: Final-year nursing students encountered psychological, social, and structural challenges during their clinical placements in the early months of the pandemic outbreak. The findings underscored the need to strengthen material, academic resources, and mental health support services. Future research should focus on the experiences of registered nurses and male nurses during the pandemic. Cultivating an empowered and resilient nursing workforce is imperative to combat future healthcare challenges in Guyana.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| 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".