Mentorship in nursing academia: A qualitative study and call to action
Bibliographic record
Abstract
Objective: Around the world nursing education institutions have been calling for mentorship; however, little is known about nursing faculty member’s perspective on if and why mentorship is important and at what career stages it is most valuable. The purpose of this study is to describe the nursing faculty member’s perspectives on mentorship in Canadian schools of nursing and explore if, why, and when mentorship is perceived to be needed.Methods: A qualitative thematic analysis study was conducted. Participants were purposively samples from the 81 English-speaking schools to capture variation across rank, tenure, school size, and areas within Canada. Semi-structured telephone interviews were conducted with 48 nursing faculty members from across Canada. Interview data was thematically analyzed.Results: Mentorship was identified as being essential yet widely absent from academic nursing. Participants viewed mentorship as a professional responsibility, and vital in consideration of the nursing faculty shortage and potential impact on students. There was an expressed need for mentorship during transition, advancement, collaboration, and as a means of way finding essential resources.Conclusions: Identifying nursing faculty member’s perspectives on mentorship is an important first step in developing mentorship in academic nursing. Nursing faculty views should be considered in the development, execution, and evaluation of mentorship programs.
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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 | 0.041 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.027 | 0.016 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.005 |
| 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".