The effectiveness of peer mentoring in promoting a positive transition to higher education for first-year undergraduate students: a mixed methods systematic review protocol
Bibliographic record
Abstract
BACKGROUND: The global transfer of nursing and midwifery education to higher education institutes has led to student nurses and midwives experiencing challenges previously faced by traditional third-level students, including isolation, loneliness, financial difficulties and academic pressure. These challenges can contribute to increased stress and anxiety levels which may be detrimental to the successful transition to higher education, thus leading to an increase in attrition rates. Peer mentoring as an intervention has been suggested to be effective in supporting students in the transition to third-level education through enhancing a sense of belongingness and improving student satisfaction, engagement and retention rates. This proposed systematic review aims to determine the effectiveness of peer mentoring in enhancing levels of student engagement, sense of belonging and overall satisfaction of first-year undergraduate students following transition into higher education. METHODS: MEDLINE, Web of Knowledge, ProQuest, Embase, CINAHL, ERIC, PsycINFO and CENTRAL databases will be searched for qualitative, quantitative and mixed methods studies on the implementation of peer assessment strategies in higher education institutes (HEIs) or universities for full-time, first-year adult students (>17 years). Included studies will be limited to the English language. The quality of included studies will be assessed using a validated Mixed Methods Appraisal Tool (MMAT). The findings will be presented as a narrative synthesis or meta-analysis as appropriate following sequential explanatory synthesis. DISCUSSION: The review will provide clear, non-biased evidence-based guidance to all third-level educators on the effectiveness of peer-mentoring programmes for first-year undergraduates. The review is necessary to help establish which type of peer mentoring is most effective. The evidence from qualitative and quantitative studies drawn from the international literature will be utilised to illustrate the best way to implement and evaluate peer mentoring as an effective intervention and will be useful in guiding future research and practice in this area. These findings may be applied internationally across all disciplines.
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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 | 0.130 | 0.121 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.017 | 0.019 |
| Bibliometrics | 0.018 | 0.014 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.043 | 0.006 |
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".