Factors that impact implementation of mentorship programs in nursing academia: A sequential-explanatory mixed methods study
Bibliographic record
Abstract
Objective: Nursing education institutions globally have called for mentorship to help address the nursing faculty shortage; however, little is known about the current state of mentorship or the barriers and facilitators for implementing mentorship programs in Canadian nursing schools. The objective of this study is to identify the current state of mentorship in Canadian nursing academia and explore factors that impact implementation of mentorship programs.Methods: A sequential-explanatory mixed methods design with 2 phases: (1) cross sectional survey of nursing faculty to identify the current state of mentorship in Canadian schools of nursing and explore facilitators and barriers to implementing mentorship programs; and, (2) qualitative interviews about nursing faculty member’s experiences with mentorship. Integration occurred when selecting phase 2 participants based on phase 1 results. A web-based survey was developed based on a review of literature; surveys were collected between September 2015 and March 2016 from 935 nursing faculty members. Survey participants (n = 48) were purposively sampled for maximum variation to participate in qualitative interviews, and data were analyzed thematically.Results: The majority of Canadian nursing schools lack formal mentorship programs with existing mentorship programs varying in scope and components. Factors influencing mentorship program implementation include: mentorship training and guidelines; quality of the mentoring relationships; choice and availability of mentors; organizational support; time and competing priorities; culture of the institution; and evaluation of mentorship outcomes.Conclusions: Our results confirm lack of formalized mentorship programs in Canadian schools of nursing. In developing mentorship programs, academic leaders need to consider multiple barriers and facilitators to ensure success.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".