“WHEN I SAID I WANTED TO DIE AT HOME, I DIDN’T MEAN A NURSING HOME”: END-OF-LIFE CARE TRAJECTORIES
Bibliographic record
Abstract
Given concerns around population aging and future health care costs, issues around end-of-life care have attracted increasing attention in recent years. Although the focus tends to be on reducing hospital deaths and increasing those that take place in people’s own homes, many older adults end their lives in nursing home care. Yet, little is known regarding the pathways that lead older adults to end their lives in these settings, nor the factors that influence them. This study draws on a structural life course perspective and administrative data to examine the long-term care (LTC) trajectories experienced by older adults who end their lives in nursing home care (NH) and compare them to those of LTC recipients who end their lives in home and community-based care (HCC) or hospital care (HC) settings. The overall sequencing of care transitions is considered along with the role of social structural factors, social and economic resources, and health factors in influencing them. Data were obtained from client assessments on individuals aged 65+ who received publicly-subsidized LTC services in one Canadian health region and who died between April 1, 2008 and December 31, 2012 (n=13,466). Only 10.2% of clients died at home in the community. Most died in NH settings (56.5%) or in hospital following a transfer from NH (12.0%) care. Just over one-fifth (20.9%) died in hospital following a transfer from HCC. Multinomial logistic regression analyses reveal the importance of social structural factors, social and economic resources, and health factors in shaping these trajectories. These findings support the utility of a structural life course perspective and suggest avenues for enhancing equitable and desirable end-of-life care.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".