Exploring pairing of new graduate nurses with mentors: An interpretive descriptive study
Bibliographic record
Abstract
AIMS AND OBJECTIVES: To explore mentorship pairing practices for new graduate nurses in a tertiary care hospital. BACKGROUND: Many organisations have implemented mentorship transition programmes to decrease new nursing graduate turnover in the first two years of practice. Little is known about mentorship pairing processes. DESIGN: An interpretive descriptive qualitative study was conducted in a multicampus academic health science centre in Ontario, Canada. The COREQ reporting guideline was used. METHODS: Thirty-one semistructured interviews were conducted from July 2018-July 2019 in a multicampus academic health science centre with new nursing graduates, experienced nurses and nurse leaders who participated in the New Graduate Guarantee programme or were involved in the mentor-mentee pairing process in 2016 or 2017. Data collected were analysed using thematic analysis within the groups and triangulated across groups. RESULTS: Neither the new graduates nor the mentors were aware of the pairing processes. Nursing leaders relied on their knowledge of the participants to pair new graduates and mentors with many stating participants' personalities were considered. New graduates and mentors described making an initial connection and socialisation as important themes related to facilitating the pairing process. Organisational influences on pairing included taking breaks together, the location of the final student placement, and the management of workload and scheduling. CONCLUSIONS: Increased awareness and transparency regarding nursing mentorship pairing processes is required. Pairing processes suggested by participants warrant further investigation to determine efficacy. RELEVANCE: Findings reinforce the need to discuss and research nursing specific mentorship pairing processes.
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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 | 0.031 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.012 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".