Factors Affecting Clinical Learning of Undergraduate Nursing Students in Azad Jammu and Kashmir
Bibliographic record
Abstract
Nursing education encompasses both theoretical knowledge and clinical education, equipping nursing students for their future roles and enabling them to transition from dependency to independent practice. Thus, clinical education is considered integral and irreplaceable in training nursing professionals, allowing them to attain various educational objectives, including enhanced communication skills and critical thinking. Objectives: To identify factors affecting the clinical learning of undergraduate nursing students in Azad Jammu and Kashmir. Methods: This quantitative descriptive cross-sectional design study collected data through a census sampling technique from 286 undergraduate nursing students. Inclusion criteria focused on students who attend at least one complete course of clinical rotation; students who were on leave or not willing were excluded from the study. Data was collected by using structured questionnaires, and analysis was made by using SPSS 26 version. Results: The findings revealed the challenges, such as inadequate supervision (42.7%), time constraints for nursing staff (82.2%), and student hesitation due to fear of errors (75.9%). Factors include hospital collaboration (71%), educator support (74.7%), and patient reluctance (71.7%). Correlation analysis links these factors to demographics, including supervision type and study year. Conclusions: The study findings concluded that clinical learning in nursing students is influenced by supportive environments, clear objectives, and adequate preparation, while barriers like poor supervision, discrimination, and lack of resources. These factors must be addressed through improved educator-student ratios, better training, and collaboration with clinical staff.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".