Hospice use and one-year survivorship of residents in long-term care facilities in Canada: a cohort study
Bibliographic record
Abstract
BACKGROUND: Hospice care is designed for persons in the final phase of a terminal illness. However, hospice care is not used appropriately. Some persons who do not meet the hospice eligibility receive hospice care, while many persons who may have benefitted from hospice care do not receive it. This study aimed to examine the characteristics of, and one-year survivorship among, residents who received hospice care versus those who did not in long-term care facilities (LTCFs) in Canada. METHODS: This retrospective cohort study used linked health administrative data from the Canadian Continuing Reporting System (CCRS) and the Discharge Abstract Database (DAD). All persons who resided in a LTCF and who had a Resident Assessment Instrument Minimum Data Set Version 2.0 (RAI-MDS 2.0) assessment in the CCRS database between Jan. 1st, 2015 and Dec 31st, 2015 were included in this study (N = 185,715). Death records were linked up to Dec 31th, 2016. Univariate, bivariate and multivariate analyses were performed. RESULTS: The reported hospice care rate in LTCFs is critically low (less than 3%), despite one in five residents dying within 3 months of the assessment. Residents who received hospice care and died within 1 year were found to have more severe and complex health conditions than other residents. Compared to those who did not receive hospice care but died within 1 year, residents who received hospice care and were alive 1 year following the assessment were younger (a mean age of 79.4 [+ 13.5] years vs. 86.5 [+ 9.2] years), more likely to live in an urban LTCF (93.2% vs. 82.6%), had a higher percentage of having a diagnosis of cancer (50.7% vs. 12.9%), had a lower percentage of having a diagnosis of dementia (30.2% vs. 54.5%), and exhibited more severe acute clinical conditions. CONCLUSIONS: The actual use of hospice care among LTCF residents is very poor in Canada. Several factors emerged as potential barriers to hospice use in the LTCF population including ageism, rurality, and a diagnosis of dementia. Improved understanding of hospice use and one-year survivorship may help LTCFs administrators, hospice care providers, and policy makers to improve hospice accessibility in this target group.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".