Evaluation of a mentorship program for new and more-experienced nursing faculty
Bibliographic record
Abstract
Objective: Evaluation of a nursing faculty mentorship program available to every faculty member regardless of seniority.Methods: Design: The faculty mentorship program was developed and implemented in a university-affiliated nursing department in Montreal in 2018. Mentors and mentees evaluated the program using self-reported surveys one-year post-implementation. The surveys addressed three main themes: (a) determining goals of the mentoring partnership; (b) overall level of satisfaction with the program; and (c) characteristics of each dyad’s mentoring agreement (strategies used to communicate, where the dyads met, etc.). Method: Quantitative and qualitative analyses were conducted.Results: A total of 19 mentees and 15 mentors completed the survey. The program was judged to be a worthwhile use of time by 89% of mentees and 93% of mentors. Even though specific mentoring needs differed according to seniority level, the program was shown to be beneficial to both less-experienced and more-experienced faculty. The main barriers to mentorship that were identified were related to timing, scheduling and time commitment issues. Clinical relevance: A successful mentorship program aimed at fostering relationships between nursing faculty members, regardless of seniority, has the potential to improve the productivity of a healthy workplace including the quality of both teaching and research.Conclusions: The mentorship program proposed herein was found to be useful and effective, as well as being beneficial as much for younger as for more experienced faculty members.
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 | 0.012 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".