Co-designing a workforce retention framework for long-term care homes
Bibliographic record
Abstract
Abstract Background Despite the presence of long-standing staffing issues and evidence of turnover impacting quality of resident care, there is little guidance to assist long-term care (LTC) homes to create sustainable staffing stability and retention programs. The purpose of this study is to co-develop a workforce retention framework for LTC leaders to help guide staffing stability and retention programs in their nursing department. Methods A modified Delphi study was used to obtain consensus of workplace factors for retention of nurses and personal support workers in LTC. Forty staff members shared their level of agreement through Delphi surveys. Drawing from the literature, 54 item statements for the first survey round were organized into six categories - work/life balance, scheduling policies and procedures, professional development and education, work conditions, quality of care, and communication. Results Study reveals resilience level factors that acknowledge the challenges of LTC work and impact on individual health and wellbeing. It appreciates that the nature of the work environment can be unpredictable as resident care complexities increase, practice guidelines and ministry mandates changes, and the implications of the prolonged staffing issue. Continuous learning reflects the quality assurance and accountability of all practicing nurses to reflect on their current practice, identifying learning needs to actively update their knowledge and skills needed for continuing competence. Conclusion This study has implications for comprehensive staff retention planning and program development in LTC. As demand for LTC services rises, comprehensive staffing retention programs are necessary to sustain skilled and experienced staff in the workforce.
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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 | 0.036 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.002 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".