Virtual mentoring in nursing education: A scoping review of the literature
Bibliographic record
Abstract
Background and objective: Large numbers of nurse educators are retiring, creating a paucity of experienced advisors and research investigators to mentor nurse educators. The guiding of doctoral students in nursing is at a demanding crossroad. A new mentoring approach is needed within nursing education to support doctoral students who wish to transition to nursing education and current nurse educators who wish to further the science of nursing education. A scoping review was conducted to determine what is currently known from the existing literature about virtual mentoring in nursing education.Methods: Literature published between 2012 and 2017 was reviewed from two electronic databases using the key words virtual mentoring, e-mentoring, cyber mentoring, online mentoring, tele-mentoring, nursing education, and college or university or higher education. The framework from Arksey and O’Malley was utilized for this study.Results: Two themes have been identified: Technological Support for the Virtual Mentoring Role and Evolving Virtual Mentoring Programs in Nursing Education.Conclusions: The available current research fails to adequately answer the research question. Further research into doctoral nursing graduates lived experience of a formal virtual mentoring program and building upon the virtual mentoring experience is needed.
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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 | 0.009 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.015 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".