Peer tutoring in nursing: Quantitative evaluation of a formalized undergraduate tutoring program
Bibliographic record
Abstract
Background and objective: Based on the limited literature, a formalized peer tutoring program was developed at the study institution’s School of Nursing to promote the success of academically at-risk students. The evaluation process was designed to guide program improvement as well as to contribute to the available literature related to peer tutoring in programs of nursing. The purpose of this study was to formally evaluate a newly developed formalized peer tutoring program for undergraduate nursing students, to inform other undergraduate nursing programs considering implementing a peer tutoring program.Methods: The peer tutoring program was evaluated using parallel post-experience surveys for tutors and tutees. Participants also completed a Learning and Studying Strategies Questionnaire, to determine if strategy use differed between the two groups.Results: There were no statistically significant differences in learning/studying strategies used by tutors and tutees, with both being predominantly superficial strategies. Tutors and tutees evaluated the tutoring program overwhelmingly positively. A few students did make suggestions for improvements in the payment system and suggested making tutoring more widely available.Conclusions: The formalized peer tutoring program is a valuable asset in promoting the academic success of undergraduate nursing students. Minor changes to the program have been made according to student suggestions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.014 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".