Understanding Nurse Retention at a Mental Health and Addictions Facility During a Dual Pandemic
Bibliographic record
Abstract
BackgroundThe COVID-19 pandemic exposed nurses to new and more severe workplace stressors; exposure to these workplace stressors has exacerbated nurse turnover. Nurses working in mental health and substance use (MHSU) have also experienced the unique stressor of the overdose crisis in British Columbia (BC). MHSU nurses have been at the forefront of working to manage these dual emergencies. There is limited evidence related to the compounding effect of COVID-19 and the overdose crisis on nursing turnover. Understanding the unique conditions that MHSU nurses are currently experiencing and what factors influence a nurse's intention to stay in or leave a healthcare facility is essential in developing strategies to minimize turnover and maximize retention.PurposeTo explore the factors that affect nurse turnover while working through the dual emergencies within a MHSU facility in BC, Canada.MethodsA qualitative descriptive approach with an inductive, descriptive thematic analysis guided this quality improvement project.ResultsFindings were grouped into two main themes: reasons for leaving and reasons for staying. Reasons for leaving included workplace safety, seeking new opportunities, lack of support, and being short-staffed. Reasons to stay encompassed connections with clients, leaders and colleagues, support from colleagues and leaders, and feeling valued, safe, and heard.ConclusionsPerceived personal safety and protection from workplace violence were found to increase the likelihood of intent to leave and turnover among nurses. Further, psychosocial safety and connection among nurses and health leaders were found to decrease the likelihood of turnover.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".