Evaluating the Economic loss of Caregiving for Palliative Care Patients
Bibliographic record
Abstract
OBJECTIVE: Our aim is to provide a unified measure of the economic burden faced by families during the palliative phase of care and to compare this measure to Statistics Canada's low-income cut-off. METHODS: Samples of palliative care patients living at home and their main informal caregivers were recruited in five Canadian urban regions. Interviews were performed every two weeks until the patient's passing, up to a maximum of six months. Participants were asked to provide details about their expenses and their absences from work that related specifically to the patient's condition. Income loss was evaluated for 192 family units. RESULTS: About 9 percent of families incurred economic losses in excess of 10 percent of their pre-study gross annual income; low-income status increased from 27 (before) to 40 (after). CONCLUSION: This is the first study to provide a unified measure of economic losses of caregiving that can be related to a publicly designated low-income threshold.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| 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".