Participatory Strategies to Enhance Resilience and Job Satisfaction and Reduce Stress to Mitigate Early Retirement Intentions Among Nurses: Protocol for a Qualitative Study (Preprint)
Bibliographic record
Abstract
BACKGROUND As Canada’s population ages, so does its workforce. Early retirement among nurses is on the rise and has become the norm within this workforce. It represents a major concern for maintaining an adequate and qualified workforce. On the one hand, the decision to take early retirement can be influenced by various factors, including occupational stress. By contrast, low job satisfaction can exacerbate early retirement intentions, while resilience is positively associated with the intention to remain at work. Little is known about how to mobilize these factors to promote healthy job retention for nurses as they age. OBJECTIVE This study aims to (1) explore the experiences of older nursing staff regarding their intention to take early retirement and the influence of occupational stress, resilience, and job satisfaction; (2) explore interventions used to optimize the influence of resilience and job satisfaction and minimize the influence of occupational stress on early retirement; and (3) generate and validate participatory strategies tailored to the context of older nursing staff to optimize the influence of resilience and job satisfaction and minimize the influence of occupational stress on early retirement. METHODS A 3-phase qualitative research design will be used. In phase 1, we will use an interpretive descriptive design using semistructured interviews to explore the experience surrounding early retirement intentions and related factors among nurses. In phase 2, we will use a scoping review to explore interventions used to optimize the influence of resilience and job satisfaction and minimize the influence of occupational stress on early retirement. In phase 3, we will use the technique for research of information by animation of a group of experts method with a group of 8 participants. This method will allow us to generate and validate participatory strategies tailored to the context of older nurses. RESULTS Initial results are expected in August 2025. The findings of this study will be shared through multiple platforms to maximize their reach and impact. This will include publishing scientific articles, completing a research dissertation, and presenting at conferences. A concise summary document highlighting key findings will be sent to study participants, who will also have the option to receive links to the online publications derived from the research. CONCLUSIONS This protocol presents detailed information about the entire structure of the 3-phase research project. Studying early retirement issues among older nurses is essential. It promotes their health, retention, and inclusion, and recognizes their contributions to the sector. INTERNATIONAL REGISTERED REPORT PRR1-10.2196/72089
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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 | 0.048 | 0.046 |
| Meta-epidemiology (narrow) | 0.003 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.064 | 0.008 |
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".