EXAMINATION OF STAFFING STABILITY IN LONG-TERM CARE
Bibliographic record
Abstract
Abstract Background Long-term care (LTC) homes in Ontario, Canada face challenges of recruiting and retaining qualified staff prepared to address resident care needs. Objectives To gain knowledge of the barriers and facilitators associated with recruiting and retaining nurses (RPNs/RNs) and personal support workers (PSW) in LTC. Method Online survey questionnaires were distributed to staff working in a municipal home, and to PSW and nursing students. Staff participants (n=93) ranked how they perceived their work in LTC during COVID-19 to inform staffing stability efforts, and were invited to elaborate further on their responses using open-ended questionnaire. Nursing student participants (n=11) ranked how they perceived working in LTC. Results Findings revealed that staff participants (57%) reported intentions to remain in their LTC employment, leaving about 43% of the workforce at risk of leaving. Four themes emerged from this study: (1) Embracing resident centred care as the top priority; (2) Rebuilding a health workplace through enhanced leadership and organizational support; (3) Promoting quality of care through open communication and professional development opportunities; and (4) Transforming work scheduling policies and staffing practices to support workforce retention. Conclusion Senior leaders and LTC organizations play a critical role in refocusing staffing stability efforts. To help make LTC a workplace of choice, major changes must be considered to include greater visibility and presence of leadership, ongoing training and education, and revisit scheduling policies and practice with input from part-time and casual 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 | 0.003 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".