Needs and experiences of postgraduate nursing students in Nigeria during the COVID‐19 pandemic
Bibliographic record
Abstract
AIM: To explore the experiences and needs of postgraduate nursing students within the Nigerian context. DESIGN: This qualitative study was conducted using a descriptive phenomenological approach. METHOD: Data were collected between February and April 2022 using a purposive sampling method and telephone semi-structured interviews. Colaizzi's method of Qualitative data Analysis was utilized. Twenty-two Nigerian postgraduate nursing students were interviewed. RESULTS: Three themes emerged: challenges of Nigerian postgraduate students before the pandemic, the impact of the pandemic on postgraduate education, and innovations to improve postgraduate education in Nigeria. The challenges include the burden of physical lectures, lack of infrastructure, and poor mentorship of postgraduate nursing students. The impact of the pandemic on postgraduate education includes abrupt disruption of the academic program, a prolonged academic calendar, and a communication gap between students and their research supervisors. Innovations to improve postgraduate nursing education in Nigeria also include adoption and sustainability of e-learning, upgrading post-basic to postgraduate nursing programmes, proper structuring of postgraduate nursing education, commencement of postgraduate nursing programmes in more universities and provision of financial aid for students. Our primary finding is that funding, mentorship and infrastructure were issues peculiar to all the respondents. CONCLUSION: This study concludes that efforts should be made to maintain a seamless educational program by ensuring an uninterrupted flow of learning through virtual means, thereby enhancing effective teaching and learning. IMPLICATIONS: Graduate nursing studies is one of the suggested solutions in the WHO strategic direction for nursing and midwifery globally to achieve Universal Health Coverage . The reason is that nurses can practice with more and better skills in any work setting, thus improving the quality of health care services. Our study provides insights into the experiences of postgraduate students and how these could discourage other nurses who might have thought about furthering their studies. Efforts should be made to provide all the support that these students need, using evidence from this study and similar studies to ensure they have a good learning experience and others can be motivated to learn at the graduate level as well. This will increase the proportion of nurses and midwives honed with better skills to provide more standard quality services that will improve patient care outcomes.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".