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 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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".