Development of a Nursing Mentorship Program in a Haematology/Oncology/Blood and Marrow Transplant and Cellular Therapy at a Tertiary Paediatric Hospital
Bibliographic record
Abstract
BackgroundNavigating the first year of nursing employment in the subspecialty of Paediatric Haematology/Oncology/Blood and Marrow Transplant and Cellular Therapy (H/O/BMT/CT) is overwhelming and challenging for novice nurses. Ill-equipped with strategies to manage the challenges of this population, there is a significant negative impact on nursing resiliency. In the literature, mentorship has been shown to ease the transition from new graduate to independent nurse.MethodA nursing-led group in the H/O/BMT/CT program at the Hospital for Sick Children (SickKids) developed a structured program of formal mentor-mentee relationships to support novice nurses. This pilot initiative was implemented during the first year of independent practice after completion of nursing orientation.ResultsA survey, with a 53% response rate, was conducted at the 1-year mark of the pilot project. The responses revealed that 89% of novice nurses found the relationship supportive and preferred ongoing mentorship beyond their first year.DiscussionEstablished in 2019, this program has since evolved in the H/O/BMT/CT division. Mentor-mentee dyads reported discussing work-life balance, how to build confidence, navigating challenging families, and more. Mentees also found that their mentor was accessible and approachable throughout the relationship. This pilot project demonstrated through two surveys and a mentor focus group that a formal mentorship program is feasible and is a welcomed layer of support by novice 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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".