Students Empowering Students Through Peer Mentorship: An Untapped Resource
Bibliographic record
Abstract
Peer mentoring (PM) builds connections and promotes academic excellence by supporting students transitioning into higher education (Carragher & McGaughey, 2016). PM programs in nursing has also been reported to nurture nursing students’ professional identities (Lombardo, Wong, Sanzone, Filion, & Tsimicalis, 2017). Nursing students help their peers understand, critique, and resolve professional identity questions that arise throughout their undergraduate preparation (Price, 2009)..While a current PM committee within a Faculty of Nursing has successfully engaged the student body, it remains to be an untapped resource. Students with similar experiences can offer support regarding academics and provide important insight regarding the demands of the profession.An opportunity exists for peer mentors, mentees, and faculty members to become co-inquirers in exploring the nature of nursing and influence teaching and learning experiences in higher education. With students as drivers, PM has the potential to create a self-sustaining environment where strengthened and genuine student-teacher connections are privileged.
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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 | 0.017 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.011 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.004 | 0.038 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.028 | 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".