Peer Mentorship for the Internationally Educated Nurse: An Appreciative Inquiry
Bibliographic record
Abstract
Within nursing, mentorship between the graduate and student nurse relationship has been a longstanding strategy to support students' clinical placement knowledge and praxis.More recently, peer mentorship between the student-to-student relationships has gained interest within academic settings.However, this area has little research focusing on mentorship processes and outcomes; particularly those related to Internationally Educated Nurses (IENs) and their transitioning into being a learner within an academic setting while acclimatizing to Canadian life.This exploratory project, using an appreciative inquiry (AI) approach, focuses on the peer-mentorship aspect of the student nurse advocacy program (SNAP) with IENs registered as learners at Langara College in the School of Nursing.Specifically, this research project identifies the strengths and needs of IENs registered in a post-degree certificate program, as well as those of their peer mentors.A purposive sample of eight participants voluntarily enrolled in the project wherein each IEN was paired with a peer mentor from SNAP, a Bachelor of Science in Nursing, student.A thematic analysis of data obtained from focus group discussions and a survey questionnaire suggest all participants bring a number of strengths to the mentorship process.Four themes were identified: cultural understanding, trust and support, college integration, and blending of roles.This last theme, blending of roles, provides an alternate view of what is meant by mentorship.Furthermore, the findings suggest key attributes simultaneously support the transition of IENs and build upon the capacities of the peer mentors.Participants identified strategies that can enhance the resources and services provided by SNAP which will inform future IEN education.These strategies and the continued analysis of data will be further explored in phases design and destiny of Appreciative Inquiry (AI) in the next stage of the project.
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 | 0.019 | 0.030 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.001 | 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".