Quality Indicator Rates for Seriously Ill Home Care Clients: Analysis of Resident Assessment Instrument for Home Care Data in Six Canadian Provinces
Bibliographic record
Abstract
Background: Few measures exist to assess the quality of care received by home care clients, especially at the end of life. Objective: This project examined the rates across a set of quality indicators (QIs) for seriously ill home care clients. Design: This was a cross-sectional descriptive analysis of secondary data collected using a standardized assessment tool, the Resident Assessment Instrument for Home Care (RAI-HC). Setting/Subjects: The sample included RAI-HC data for 66,787 unique clients collected between January 2006 and March 2018 in six provinces. Individuals were defined as being seriously ill if they experienced a high level of health instability, had a prognosis of less than six months, and/or had palliative care as a goal of care. Measurements: We compared individuals with cancer (n = 21,119) with those without cancer (n = 47,668) on demographic characteristics, health-related outcomes, and on 11 QIs. Results: Regardless of diagnosis, home care clients experienced high rates (i.e., poor performance) on several QIs, namely the prevalence of falls (cancer = 42.4%; noncancer = 55%), daily pain (cancer = 48.3%; noncancer = 43.2%), and hospital admissions (cancer = 48%; noncancer = 46.6%). The QI rates were significantly lower (i.e., better performance) for the cancer group for three out of the 11 QIs: falls (absolute standardized difference [SD] = 0.25), caregiver distress (SD = 0.28), and delirium (SD = 0.23). Conclusions: On several potential QIs, seriously ill home care clients experience high rates, pointing to potential areas for quality improvement across Canada.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".