Measuring the Use of End-of-Life Symptom Relief Medications in Long-Term Care Homes—a Qualitative Study
Bibliographic record
Abstract
Background: At the end of life, individuals may experience physical symptoms such as pain, and guidelines recommend medications to manage these symptoms. Yet, little is known about the symptom management long-term care (LTC) residents receive at the end of life. Our research team developed a metric-whether residents receive one or more prescriptions for an end-of-life symptom management medication in their last two weeks-to explore end-of-life care for LTC residents. This qualitative study aimed to inform the refinement of the end-of-life prescribing metric, including the acceptability and applicability to assess the quality of a resident's symptom management at end-of-life. Methods: We conducted 14 semi-structured interviews with Ontario health-care providers (physicians and nurses) who work in LTC homes and family caregivers of residents who died in LTC. Interviews were conducted virtually between February 2021 and December 2022, and were analyzed using thematic analysis. Results: We identified three major themes relating to perceptions of the metric: 1) appropriateness, 2) health-care provider applicability, and 3) caregiver applicability. Participants noted that the metric may be appropriate to assess end-of-life care, but noted important nuances. Regarding applicability, health-care providers found value in the metric and that it could inform their practice. Conversely, caregivers found limited value in the metric. Conclusion: The proposed metric captures a very specific aspect of end-of-life care-whether end-of-life medications were prescribed or not. Participants deemed that the metric may reflect whether LTC homes have processes to manage a resident's end-of-life symptoms with medication. However, participants thought the metric could not provide a complete picture of end-of-life care and its quality.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".