Group mentorship programme for graduating nursing students to facilitate their transition: A pilot study
Bibliographic record
Abstract
AIM: The present study aims to describe and evaluate a group mentorship programme for graduating nursing students following the first pilot testing. BACKGROUND: A mentoring relationship has been found to be beneficial in easing the challenging transition from nursing student to nurse. However, very few mentoring programmes have been formally developed to pair students with clinical nurses before graduation. METHODS: A group mentoring programme for graduating nursing students was developed where clinical nurse mentors met with student mentees twice before graduation and once shortly after graduation to address relevant challenges. Mentees and mentors completed a survey after the last session. RESULTS: Eighteen mentees and 12 mentors completed the survey. Results suggest a high level of satisfaction with the programme from both mentees and mentors, as well as a positive impact on mentees' transition into the workplace and levels of stress and self-confidence. CONCLUSIONS: The pilot testing of the group mentorship programme is believed to have been successful. IMPLICATIONS FOR NURSING MANAGEMENT: This pilot project highlights the value to nursing leadership of group mentoring partnerships between academic and clinical settings, which can improve the integration of new nurses in the workplace and increase mentors' awareness of the needs of these nurses.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".