Does informal care impact utilisation of home-based formal care services among end-of-life patients? A decade of evidence from Ontario, Canada
Bibliographic record
Abstract
Understanding how informal care impacts formal care utilisation for home-based end-of-life patients is an important policy- and practice-relevant question. This paper aims to assess the relationship between informal and formal home care among home-based end-of-life patients and how this relationship has changed over the last decade and over the end-of-life trajectory. We focus on informal care provided by family members or friends, and three types of home-based formal care services: care by personal support workers, physician visits, and nurse visits. Using survey data collected in a home-based end-of-life care programme in Ontario, Canada from 2005 to 2016, we build a two-part utilisation model analysing both the propensity to use each type of formal care and the amount of formal care received by patients. The results suggest that informal care is a substitute for care by personal support workers, but a complement to physician visits and nurse visits. In the case of nurse visits, an increased complementary effect is observed in more recent years. For home-based physician and nurse visits, the complementary effect grows with patient's proximity to death. These results highlight the complexity of the relationship between informal and formal care among home-based end-of-life patients. Decision-makers need to take into account the relationship between informal care and different types of formal services when introducing future policies.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".