“Help with rowing the boat”: Implementing and evaluating the Strengthening a Palliative Approach in Long-Term Care program in four Canadian provinces
Bibliographic record
Abstract
Background: Despite high mortality rates in long-term care (LTC), LTC homes continue to struggle to implement a palliative approach to care. Objectives: The objective of this research was to implement and evaluate the Strengthening a Palliative Approach in Long-Term Care (SPA-LTC; www.spaltc.ca) program. Specifically, we explored its feasibility, acceptability, and preliminary effects on resident comfort, use of emergency department at end-of-life (EOL), and location of resident death. Design: This study used an explanatory mixed method design in four LTC homes; one in each of four provinces (Ontario, Manitoba, Saskatchewan, Alberta) in Canada to assess acceptability, feasibility, and preliminary effects of the program. Methods: Quantitative and qualitative data were collected whereby the qualitative component was used to help explain or elaborate on the main quantitative components. Results: Of the 102 participating residents, 74.5% (76/102) had a Palliative Care Conference (PCC). However, of those who died, only 68.8% of them had a PCC. Rates of hospital use were reduced for study participants in terms of emergency department visits at EOL (relative risk reduction (RRR): 46%; 95% CI: -1.12, -0.10) and hospital deaths (RRR: 88%; 95% CI: -4.06, -1.12) compared to baseline. However, there were no significant differences in resident comfort. Family members stated that the PCCs were informative and thought that good communication was critical in providing quality care. They highlighted that close relationships and mutual respect among staff, residents, and families led to more meaningful care while the resident was alive as well as into bereavement. Staff stated that they found the SPA-LTC resources helpful and recognized the importance of having strong leadership using a Palliative Champion Team. Conclusion: The SPA-LTC program appears to be feasible on some key activities and supports a family-centered approach to care, which relies on strong communication. Future research is needed to confirm these initial results.
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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.010 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".