Near Peer Learning To Facilitate Nursing Students’ First Medical Surgical Clinical Experience
Bibliographic record
Abstract
Introduction and Background: Ensuring an appropriate clinical experience for students is becoming more challenging in the context of a global nursing shortage, more acutely ill clientele, and limited numbers of educators, academics and clinical instructors. Near Peer Learning Activities (NPLA) have been shown to be effective and may help students to feel prepared and confident to begin the clinical rotation. This study explored nursing students’ experiences in their first medical-surgical practicum following a NPLA involving Health and Physical Assessment (HPA). Methods: Educators at one Canadian University recently designed and implemented a NPLA in the clinical setting where junior nursing students performed a focused health and physical assessment on a patient, in an acute medical-surgical unit with the guidance of senior students. Ten nursing students who took part in the NPLA were individually interviewed. Thematic content analysis was used to generate the themes. Findings: Following the NPLA, two main themes captured the essence of the students’ experience: (1) making nursing real through near peer learning and (2) surmounting personal and contextual challenges in a first medical-surgical experience. Students “owned” their HPA skills, felt excited and prepared entering their medical-surgical placement. During the experience, however, students worried about not measuring up and the unreceptive learning environment. Conclusion: The NPLA provided a safe context for novice students to apply HPA and become familiar with the hospital setting, facilitating their transition into a challenging first medical-surgical practicum.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".