Strategies to attract and retain infection prevention and control nurses in the era of nursing shortage
Bibliographic record
Abstract
Background: One of the major challenges faced by healthcare facilities in Quebec, Canada, is the shortage of nurses specializing in Infection Prevention and Control (IPC), exacerbated by the broader nursing shortage. This survey aimed to identify effective strategies for retaining IPC nurses in Montreal, Canada, amidst the ongoing staff shortages and frequent rotations. Methods: A descriptive cross-sectional survey was conducted with a convenience sample of IPC nurses in Montreal, utilizing both open- and closed-ended questions. The survey was developed following a literature review. A pilot testing was followed by an open-ended survey in 2023 targeting IPC and clinical managers to gather further perspectives. Quantitative analysis was applied to the closed-ended responses, while thematic analysis was conducted on the open-ended responses. Bivariate analysis was employed to explore relationships between variables. Results: Quantitative and qualitative analyses identified five key strategies to address the IPC nurse shortage. Respondents, including IPC nurses and managers highlighted the importance of integrating non-nursing professionals into the IPC roles. A significant concern among IPC nurses was the lack of funding for specialized training and certification. Other retention challenges included inadequate salary recognition for educational qualifications, limited opportunities for career advancement, and insufficient organizational recognition of the importance of infection control. Moreover, the work environment was deemed essential for retaining IPC professionals. Conclusions: The findings from the current study provide valuable insights for policymakers seeking to improve IPC staff retention. Addressing Quebec’s nursing shortages involves integrating non-nursing professionals into IPC roles and implementing retention strategies tailored to generational needs, such as horizontal promotions linked to progressive salary scales and educational requirements. Additionally, fostering a supportive work environment with work-life and study balance, flexible scheduling, and strong organizational recognition, while cultivating a robust infection control culture, is crucial for retaining IPC staff.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".