Predictors of caregiver distress among palliative home care clients in Ontario: Evidence based on the interRAI Palliative Care
Bibliographic record
Abstract
OBJECTIVE: This study examines predictors of caregiver distress among community-based palliative care clients. Analyses are based upon interRAI Palliative Care (interRAI PC) assessment data from palliative home care programs in three regions in Ontario, Canada. METHOD: The study sample involved all community-based palliative care clients in Ontario who were assessed with the interRAI PC as part of normal clinical practice during pilot implementation of the instrument between 2007 and 2009 (N = 3,929). The assessments were performed by trained case managers and were used as the basis for determining service needs to be addressed through services contracted from provider agencies. The main study outcome of interest was the presence of one or more indicators of caregiver distress: helper(s) unable to continue caring activities; primary informal helper expresses feelings of distress, anger, or depression; family or close friends report feeling overwhelmed by person's illness. RESULTS: Caregiver distress was evident among about 22% of palliative home care clients. Multivariate analyses identified included clinical instability (Changes in Health, End Stage disease, Signs and Symptoms [CHESS] scale), depressive symptoms, cognitive impairment, and positive outlook as significant client-level predictors. Significant caregiver characteristics included hours of informal care. Three service use/provider variables were significant predictors of caregiver distress: the specific home care agency, hospitalizations in the last 90 days, and nursing visits. SIGNIFICANCE OF RESULTS: Caregiver distress affects approximately one in five palliative care clients in the community. This may lead to a number of adverse outcomes for the caregiver and client. The experience of distress is affected by client, caregiver, and agency characteristics that are readily identified by the interRAI PC assessment instrument. The present results point to the need for a care planning protocol that may be used on a targeted basis for clients experiencing or at elevated risk of caregiver distress.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".