The Evolving and Expanding Role of Personal Support Workers in End-of-Life Care in the Long-Term Care Setting
Bibliographic record
Abstract
Abstract Background Personal Support Workers (PSWs) play a vital role in long-term care (LTC). Initially, their role was focused on assisting residents with activities of daily living. However, as resident care needs become increasingly complex due to a rapidly aging population, PSWs’ roles have expanded. End-of-life care is now a core aspect of their responsibilities. Despite this, no formal or consistent definition of PSWs’ role in end-of-life care exists. Purpose To explore and describe the role of PSWs in providing end-of-life care in LTC. Methods A qualitative single case study was conducted using virtual data collection methods. Data sources included study site characteristics, documentation data, archival records, demographic data, interviews, and observations of physical artifacts. Results Sixteen participants, including residents, family members, and LTC staff, contributed to the study. Findings revealed that PSWs frequently engage in extra-role behaviors (i.e., tasks beyond their job descriptions) when providing end-of-life care. These behaviors were widely recognized and perceived as essential for improving care quality. Additionally, the strong familial relationships between PSWs and residents were key motivators for their engagement in extra-role behaviors. Conclusions This study underscores PSWs’ evolving and expanding role in end-of-life care and highlights their indispensable contributions to LTC. By formally recognizing and integrating PSWs’ end-of-life care responsibilities into policies, training, and workforce planning, healthcare leaders can enhance the quality of care for residents while fostering a more supportive and sustainable work environment for PSWs. This change is critical for addressing workforce shortages and ensuring high-quality end-of-life care in the LTC setting.
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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".