Solving the Challenges of Learning Fundamental Clinical Skills among Nursing Students: An Action Research Approach
Bibliographic record
Abstract
Background: Despite the rapid development of innovative teaching methods in practical nursing education, acquiring clinical skills remains a significant educational challenge. It is essential to identify and solve the challenges of nursing students in learning and promoting fundamental clinical skills. Accordingly, this study aimed to explore and address the challenges of learning fundamental clinical skills among nursing students. Methods: This action research, conducted in 2023, employed two iterative cycles using both qualitative and quantitative approaches. The participants included 32 nursing students, the director of the Clinical Skills Unit, and academic researchers serving as facilitators. Qualitative data were collected through in-depth interviews, focus group discussions, observations, and field notes. For the quantitative phase, a checklist was used to assess the students’ satisfaction. Data analysis involved descriptive statistics for the quantitative data and conventional content analysis for qualitative data. Results: The qualitative findings identified three main categories, including lack of feedback, superficial learning, and ineffective communication. Following the intervention, students reported improvements in interpersonal communication, receiving more effective feedback, and engaging in active learning. Quantitative results demonstrated statistically significant increases in the mean scores of factors influencing effective learning after the activities of the action research project. Conclusion: Active participation in learning fundamental skills can reduce some of the associated challenges faced by nursing students. Through reflective processes, students were able to recognize the challenges affecting their learning and subsequently improve their learning outcomes.
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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 | 0.059 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| 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".