Effects of peer mentoring on nursing students’ perceived stress, sense of belonging, self-efficacy and loneliness
Bibliographic record
Abstract
Mentorship has been around for years and has been explored in nursing education in the clinical settings. Despite evidence that indicates that the academic environment is the most common source of stress, little mentorship implementation and investigation has been done in this environment. The purpose of this research is to describe the effects of a mentorship experience on the level of perceived stress, sense of belonging, self-efficacy, and loneliness by first year baccalaureate nursing students. A quasi-experimental design was conducted. Seventy baccalaureate nursing students in the first year of their program (n = 34 in the experimental group; n = 36 in the control group) enrolled in a single baccalaureate nursing program were recruited. Third year mentors were purposefully selected by nursing professors within the program. The Perceived Stress Scale, the College Self-Efficacy Inventory (CSEI)–Revised, Sense of Belonging-Psychological, Sense of Belonging-Antecedents, and the Revised UCLA Loneliness Scale were used to evaluate the various concepts as these tools were used in previous research with college level students and deemed to be reliable and valid tools for measuring the relevant concepts. The mentorship program was statistically significant in reducing first year nursing students’ perceived stress and loneliness. It also appeared to increase their sense of self-efficacy and psychological sense of belonging. The mentorship experience could potentially enhance the student experience as well as aid the academic institution in retention and resource maximization. The focus of this research was on the academic mentoring by peers and is worth further exploration and possible wide-scale integration within nursing education.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".