An Evaluation of the Communication at End-of-Life Education Program for Personal Support Workers in Long-Term Care
Bibliographic record
Abstract
Background: Communication skills are crucial for personal support workers (PSWs) to foster therapeutic relationships with the residents and their families in the long-term care (LTC) setting. Aim: To evaluate the impact of the Communication at End-of-Life (CEoL) Education Program on the competency and confidence of PSWs working in LTC to communicate about palliative and end-of-life care, and factors affecting their involvement in palliative and end-of-life care. Setting/Participants: PSWs from 35 LTC homes in Ontario, Canada, who participated in the CEoL Education Program between January and March 2019. Design: Mixed-methods evaluation using pre- (n = 178) and post-workshop (n = 113) surveys capturing the attitudes and beliefs toward death and dying; relationships with residents and families; and PSWs' participation in end-of-life care. Follow-up interviews were conducted between February and March 2019 with 21 PSWs to examine facilitators and barriers that affected their confidence in engaging in palliative care. Results: We observed significant improvements in all three domains, with the greatest increase (11%, p < 0.001) in the proportion of participants who responded “Often” or “Always” in the participation in end-of-life care domain. Specifically, we observed PSWs' elevated confidence in speaking with families of the residents about end-of-life, discussing goals and plans with the residents, and realizing that a “good death” is possible. Time constraints and staff shortages were recurrent themes that hindered many participants' ability to provide resident-centered care. Conclusions: This evaluation demonstrates that CEoL Education Program was associated with improved PSW competency and confidence in supporting palliative and end-of-life care in LTC settings.
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 | 0.007 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".