“I didn’t know it was going to be like this.”: unprepared for end-of-Life care, the experiences of care aides care in long-term care
Bibliographic record
Abstract
BACKGROUND: Care aides provide up to 70-90% of the direct care for residents in long-term care (LTC) and thus hold great potential in improving residents' quality of life and end-of-life (EoL) care experiences. Although the scope and necessity of the care aide role is predicted to increase in the future, there is a lack of understanding around their perceptions and experiences of delivering EoL care in LTC settings. The aim of this study was to gain an understanding of the perspectives, experiences, and working conditions of care aides delivering end-of-life care in LTC in a rural setting, within a high-income country. METHODS: Data were collected over ten months of fieldwork at one long-term care home in western Canada; semi-structured interviews (70 h) with 31 care aides; and observation (170 h). Data were analysed using Reflexive Thematic Analysis. RESULTS: Two themes were identified: (i) the emotional toll that delivering this care takes on the care aids and; (ii) the need for healing and support among this workforce. Findings show that the vast majority of care aides reported feeling unprepared for the delivery of the complex care work required for good EoL care. Findings indicate that there are no adequate resources available for care aides' to support the mental and emotional aspects of their role in the delivery of EoL care in LTC. Participants shared unique stories of their own self-care traditions to support their grief, processing and emotional healing. CONCLUSIONS: To facilitate the health and well-being of this essential workforce internationally, care aides need to have appropriate training and preparation for the complex care work required for good EoL care. It is essential that mechanisms in LTC become mandatory to support care aides' mental health and emotional well-being in this role. Implications for practice highlight the need for greater care and attention played on the part of the educational settings during their selection and acceptance process to train care aides to ensure they have previous experience and societal awareness of what care in LTC settings entails, especially regarding EoL experiences.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".