Mentor Us: A Formal Mentoring Program for Nurses in Graduate Education
Bibliographic record
Abstract
Graduate education fosters unique skills including advanced communication, complex problem solving, project management experience and a complex understanding of specific fields of work (Edge & Munro, 2015). In Canada, enrollment in full-time graduate studies in nursing are increasing. As these enrollments increase, the academic community must consider the realities of graduate school, how students are socialized into faculties and most importantly how integration and community in these programs are fostered because these factors impact the graduate school experience and completion outcomes. Successful graduate students tend to have strong peer relationships, more positive integration, and personal connections with their department or faculty (Gansemer-Topf, Ross, & Johnson, 2006; Golde, 1998; Golde, 2000; Tamburri, 2013). Mentorship programs foster these close relationships among members, help to strengthen a sense of personal and professional safety, provide academic and professional support, and promote collegial interactions (Garvey & Westlander, 2013). In early 2013, four Master of Nursing (MN) students at a large western Canadian University began to conceptualize a formal mentorship program for nursing graduate students called Mentor Us, with the intent of improving student collegiality, building connections within the faculty and community, and providing opportunities for peers to connect. They envisioned and developed a voluntary mentorship program that offered the opportunity for peer-to-peer mentorship in dyads consisting of one mentor and one mentee. The program has seen success in achieving many of its aims and goals; however, the leadership team has also identified areas for future improvement including engaging specific student populations, improving mentor training initiatives and dyad matching, and sustaining the leadership structure of the program. The aims of the paper are first, to outline the current state of nursing graduate education in Canada. Second, to present the process of building and maintaining a formal mentorship program for nurses in graduate studies. Third, to describe the vision of the program moving forward, to outline lessons learned during the development and operation of the program, and to review strategies to ensure future program success. This paper provides a unique way to mitigate the concerns of nursing graduate students by fostering peer mentorship relationships and enhancing community connections. It provides a concrete example of the development of such a program and presents an honest critique about how to improve the program for future students.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.022 | 0.005 |
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".