Emergency Nursing: A Staff-Led Mentorship Program in a Tertiary Adult and Pediatric Emergency Department
Bibliographic record
Abstract
Background: Emergency Department (ED) nurses are familiar with high levels of turnover and staff burnout. All EDs have faced unprecedented stressors related to the global COVID-19 pandemic including higher patient acuity, senior staff attrition, and increased numbers of less-experienced new hires. In one combined pediatric and adult tertiary ED, senior nursing staff identified a need for integrating formalized mentorship for new staff in the current nursing orientation. Three senior, experienced, Registered Nurses (Mentor Leads) developed and implemented a volunteer-based, staff-led mentorship initiative piloted throughout 2021. Implementation: Mentor Leads conducted stakeholder consultation with recent new hires and senior staff prior to proposing the mentorship program model to management and the education team in November 2020. Support obtained allocated Mentor Leads two hours of classroom department orientation to conduct virtual presentations with new hires (Mentees). The presentation was structured as a nursing assignment simulation with opportunities for open discussion; it reviewed emergency case scenarios, prioritization/time management, department flow, multidisciplinary communication, and staff roles and resources. Then groups of two to four nursing Mentors, with at least one year of experience in the department, provided sequential in-person and email check-ins throughout the first six-months of the Mentees’ employment. Electronic materials, including mentorship expectations and resources, were disseminated to the Mentors to support them during their mentorship term. Evaluation Methods: Qualitative feedback was sought from Mentors and Mentees at the end of their mentorship term. Both were asked to describe any personal benefit gained from participation in the program, any challenges they recognized, and if/how the program impacted their future employment in the department. In addition, Mentees were asked to provide survey feedback following the orientation presentation. Results: The mentorship program was applied to all nine orientation groups hired in 2021. A total of 70 new hires participated as Mentees. Twenty-one volunteer Mentors were utilized to lead the orientation groups. Initial qualitative feedback for the orientation presentation and the six-month follow-up was universally positive. Qualitative themes included enhanced preparedness concerning department realities, feeling welcomed by staff, and improved understanding of department flow and avenues of assistance. One Mentee shared that, without the program they would have considered seeking new opportunities outside of the emergency department setting shortly after orientation. Mentors expressed a crucial need for the program and shared that their responsibilities and expectations were manageable. Mentors felt the email communication structure was less effective than the in-person check-ins. An incidental benefit of the program’s implementation was its positive effect on staff participating as Mentors. They reported that their experience with the program provided them with purpose, decreased levels of burnout, and strengthened their teaching and leadership skills. Qualitative feedback from Mentor Leads and experienced staff report that department mentorship culture as a whole has been enhanced by the program. Advice and Lessons Learned: Strong partnership with key stakeholders, including management and the nursing education team, was vital to implementing a successful mentorship program in the department. Concerns voiced by Mentors regarding Mentee overall performance and potential practice challenges were escalated to department Clinical Nurse Educators in order to provide targeted educational support for the success of staff. The role of strong and determined Mentor Leads were key to conduct the necessary building, implementation, and preservation of the program. A significant amount of invested time was required to ensure program success. Consideration should be given to fiscal resources to incentivize staff-led grassroots mentorship programs. Future program evaluation hopes to include detailed evaluation of objective data comparing levels of nurse turnover and vacancy rates pre- and post-implementation of the program, and an economic evaluation. Future opportunities for this program are to expand mentorship to staff new to resuscitation, triage, and charge nurse roles within the department, as well as replicate the work to other local and provincial EDs.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".