Enhancing Care for Long-Term Care Residents Approaching End-of-Life: A Mixed-Methods Study Assessing a Palliative Care Transfer Form
Bibliographic record
Abstract
Background: Many barriers exist in providing quality end-of-life care in long-term care (LTC), including transitions of care between acute care and LTC. Transfer forms can be beneficial in ensuring resident’s end-of-life care needs are coordinated between different settings. The NYGH-LTC Transfer Form is a newly developed tool created to enhance care for residents transferred from acute care back to their LTC home for end-of-life. Study Aim: Assess the perceived ease of use, usefulness, and care-enhancing potential of the NYGH-LTC Transfer Form by interprofessional LTC staff. Methods: The study population included interprofessional staff members at 2 LTC homes in Toronto, Canada. Quantitative data was obtained through surveys and qualitative data was obtained through focus groups. Results: There were a total of 34 participants. 79.4% of participants agreed the form was easy to use and 82.4% agreed it would improve care. Subgroup analysis demonstrated that participants with greater than 20 years experience were less likely to agree that it would improve care (p = 0.01). Qualitative analysis generated 4 themes: 1) Strengths, 2) Areas of Improvement, 3) Information Sharing, and 4) Communication. Conclusions: The NYGH-LTC Transfer Form was overall well-evaluated. The form was seen as most useful for those with less experience or less confidence in palliative care. Communication was identified as a major barrier to successful transitions of care and increased bidirectional verbal communication is needed in addition to the form.
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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 | 0.021 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 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".