Behind the Frontlines: Insights for Supporting Mental Health and Staff Retention in the Long-Term Care Workforce
Bibliographic record
Abstract
Background/Objectives: Canada’s long-term care (LTC) sector is struggling with a significant staffing crisis related to shortages, high-turnover rate, and challenging working conditions. The COVID-19 pandemic exacerbated these issues and emphasized the need for improved mental health support for LTC staff. Understanding and addressing the wellbeing of staff is important for ensuring quality of care and promoting a positive work environment for a healthy workforce. This study explored staff experiences in Canadian LTC homes during the COVID-19 pandemic and offers staff-driven recommendations to support staff mental health and retention moving forward. Methods: We applied the Collaborative Action Research (CAR) methodology to explore practical strategies with LTC staff to inform actions for change. Sixteen staff members working in two large urban Canadian LTC homes were interviewed using remote videoconferencing and phone calls to conduct one-on-one interviews. Thematic analysis was performed. Results: Our analysis identified four themes: depletion, lack of support, providing resources and sense of community. The SUPPORT framework was created based on staff recommendations to improve LTC staff mental health and retention. Conclusions: Urgent attention is needed to support the LTC workforce through practice change and improved policy. The implementation of comprehensive frameworks such as SUPPORT can play a pivotal role in fostering staff resilience, enhancing job satisfaction, and promoting a healthy workforce for aged care.
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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 | 0.009 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.022 | 0.010 |
| Scholarly communication | 0.010 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".