“I didn’t know it was going to be like this.”: End of Life Care Experiences of Care Aides Care in Long-term Care
Bibliographic record
Abstract
Abstract Background: Care aides provide upwards of 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.Methods: Data were collected over ten months of fieldwork at one long-term care home in western Canada; semi-structured interviews (70 hours) with 31 care aides; and naturalistic observation (170 hours). Data were analysed using Reflexive Thematic Analysis.Results: Three themes were identified: (i) the lack of training and preparedness for the role of EoL care; (ii) the emotional toll that delivering this care takes on the care aids and; (iii) 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 not adequate resources available for care aides’ to support the mental and emotional aspect 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: The care aides’ role in LTC is of increasing importance, especially in relation to the ageing population and the delivery of EoL care. To facilitate the health and wellbeing of this essential workforce, 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 wellbeing in this role.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 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".