Ontario's Long-Term Care Odyssey: Navigating The Challenges of Recruitment and Retention
Bibliographic record
Abstract
By 2068, it is anticipated that over 25% of Canada's population will be aged 65 or older, signifying a growing demand for long-term care services. Recent experiences during the COVID-19 pandemic have highlighted the substandard conditions within long-term care facilities in Ontario and Quebec, drawing attention to the pivotal importance of workforce retention and its direct impact on care quality. Employing a mixed-methods approach, a concise survey collected demographic details from participants, followed by semi-structured qualitative interviews with three individuals—a nurse, a personal support worker, and a recreation therapist. Numerous studies consistently underscored the negative effects of high turnover among healthcare professionals, emphasizing the loss of knowledge and weakening personal connections. Additionally, the significance of relationships cultivated between care workers and residents emerged as essential for both workforce retention and job satisfaction. This research contributes to discussions on workforce recruitment and retention in long-term care facilities, offering implications for policy and practice. The findings provide valuable insights into the critical role of caregiver-resident relationships in boosting job satisfaction and elevating the overall quality of care. Understanding specific strategies promoting workforce retention can guide targeted interventions, addressing this urgent issue and fostering a more sustainable long-term care workforce.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.022 | 0.004 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".