Exploring mentorship programs and components in nursing academia: A qualitative study
Bibliographic record
Abstract
Objective: Nursing education institutions globally have issued calls for mentorship to address the nursing faculty shortage; however, little is known about the current state of mentorship for faculty members in Canadian schools of nursing. The purpose of this study is to describe the current state of mentorship in Canadian schools of nursing and explore definitions and goals of mentorship programs, mentorship models and components, and mentorship evaluation.Methods: A qualitative descriptive study was conducted. Within the Canadian Association of Schools of Nursing there are 81 English-speaking schools of nursing and 2,284 permanent faculty members spread over four regions. Participants were recruited from the 81 schools of nursing through the CASN newsletter list serve and publically accessible email addresses. Inclusion was limited to English speaking faculty. Purposive sampling aimed to capture variation across rank and tenure, school, size and areas within Canada. Semi-structured interviews were utilized to explore the participant’s (n = 48) perspectives and involvement with mentorship. Interviews were audio-recorded and transcribed verbatim. NVivo was used to code and analyze the data for significant statements and phrases, which were organized into themes and sub-themes.Results: Mentorship remains largely informal in nursing academia without common definitions or goals. Current mentorship in nursing academia employed dyad, peer, group, constellation, and distance mentorship models. Common mentorship program components included guidelines, training, professional development workshops, purposeful linking of mentors and mentees, and mentorship coordinators. Evaluation of mentorship in nursing academia, where it exists, remains mostly descriptive, anecdotal, and lacks common evaluative metrics.Conclusions: Our results confirm mentorship in Canadian schools of nursing remains largely informal. In developing mentorship programs, academic leaders need to consider the mentorship models and components to meet their specific needs. Further rigorous evaluation of mentorship programs and components is needed to identify if mentorship programs are achieving specified goals.
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 | 0.026 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.017 | 0.011 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.003 | 0.008 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".