Registered Nurse Retention in Long Term Care: A Qualitative Exploration
Bibliographic record
Abstract
Registered Nurse (RN) retention continues to be an issue within long-term care (LTC). RNs play a vital role in the overall wellbeing of the aging Ontario population. In 2020, COVID-19 swept the world in a pandemic and the Ontario LTC sector was left with many changing restrictions and regulations that had a large effect on not only the RNs who work there but also the residents who resided in.\nThis study was conducted to identify what factors contribute to satisfaction and dissatisfaction for RNs in LTC. Also, it aimed to look at factors related to a RN continuing to work in LTC versus leaving the sector. The study was conducted through descriptive phenomenology. Interviews were conducted with participants who were contemplating leaving LTC and participants who were content in their positions.\nFor those content in their job relationships with residents, co-workers and management were essential for satisfaction. The Ministry of Long-Term Care (MOLTC) and wages were identified as reasons for their dissatisfaction. For the participants contemplating leaving relationships with the residents were their only source of satisfaction. Working conditions, lack of respect and the media were all seen as aiding in dissatisfaction. For both groups the theme of relationships was important to their satisfaction and the theme of systemic challenges brought up dissatisfaction.\nIncreasing meaningful connections between not only RNs and the residents but also RNs and their co-workers/management may be beneficial in increasing retention. Navigating systemic challenges through advocacy, further research and encouragement of new graduate nurses joining LTC may help to lessen the feelings of dissatisfaction with these RNs.
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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 | 0.021 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".