“The hardest job you will ever love”: Nurse recruitment, retention, and turnover in the Nurse-Family Partnership program in British Columbia, Canada
Bibliographic record
Abstract
BACKGROUND: Nurse turnover is a significant issue and complex challenge for all healthcare sectors and is exacerbated by a global nursing shortage. Nurse-Family Partnership is a community health program for first-time pregnant and parenting girls and young women living in situations of social and economic disadvantage. In Canada, this program is delivered exclusively by public health nurses and only within a research context. The aim of this article is to explore and describe factors that contribute to recruitment, retention, and turnover of public health nurses delivering Nurse-Family Partnership in British Columbia, Canada between 2013 and 2018. METHODS: Interpretive description was used to guide sampling, data collection and analytic decisions in this qualitative component drawn from the British Columbia Healthy Connections Project mixed methods process evaluation. Semi-structured, individual interviews were conducted with 28 public health nurses who practiced in and then exited Nurse-Family Partnership. RESULTS: Nurses were motivated to join this program because they wanted to deliver an evidence-based program for vulnerable young mothers that fit with their personal and professional philosophies and offered nurse autonomy. Access to program resources attracted nursing staff, while delivering a program that prioritizes maintaining relationships and emphasizes client successes was a positive work experience. Opportunities for ongoing professional development/ education, strong team connections, and working at full-scope of nursing practice were significant reasons for nurses to remain in Nurse-Family Partnership. Personal circumstances (retirement, family/health needs, relocation, career advancement) were the most frequently cited reasons leading to turnover. Other factors included: involuntary reasons, organizational and program factors, and geographical factors. CONCLUSIONS: Public health organizations that deliver Nurse-Family Partnership may find aspects of job embeddedness theory useful for developing strategies for supporting recruitment and retention and reducing nurse turnover. Hiring nurses who are the right fit for this type of program may be a useful approach to increasing nurse retention. Fostering a culture of connectivity through team development along with supportive and communicative supervision are important factors associated with retention and may decrease turnover. Many involuntary/external factors were specific to being in a study environment. Program, organizational, and geographical factors affecting nurse turnover are modifiable.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.023 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 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".