Impact of the COVID-19 pandemic on the nursing students' education in a public university in Colombia
Bibliographic record
Abstract
OBJECTIVES: To explore the impacts of the COVID-19 pandemic on nursing student education in one public university in Medellin, Colombia. METHODS: This descriptive qualitative study used content analysis to address the following questions: (1) How has the COVID-19 pandemic impacted nursing education at the University of Antioquia? (2) What were the most important challenges experienced by nursing students? (3) What was most supportive for the students during the pandemic? and (4) What were the potential opportunities and lessons learned related to nursing education? Data were collected virtually through individual online interviews with 14 undergraduate nursing students and analysed using qualitative content analysis with constant comparisons. RESULTS: Four main categories of findings related to the experience of undergraduate nursing students during the COVID-19 pandemic were identified: (1) transitioning to online learning, (2) managing the digital world, (3) impacts on clinical training, and (4) work-related stressors. Key challenges included home environments that were not conducive to learning, reduced social interactions with peers and faculty, accessing technology required for online education and insufficient preparation for clinical practice. Family members and university-provided resources were important sources of student support. Whereas the pandemic limited opportunities for hands-on clinical training, the shift to online learning allowed for the development of skills related to informational technologies and telehealth. CONCLUSIONS: Undergraduate students at the University of Antioquia identified significant barriers to learning during the COVID-19 pandemic restrictions and transition to online learning, as well as new opportunities for the development of digital skills among both students and faculty.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".