Home Care Quality Indicators (HCQIs) Based on the MDS-HC
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
PURPOSE: This study aimed to develop home care quality indicators (HCQIs) to be used by a variety of audiences including consumers, agencies, regulators, and policy makers to support evidence-based decision making related to the quality of home care services. DESIGN AND METHODS: Data from 3,041 Canadian and 11,252 U.S. home care clients assessed with the Minimum Data Set-Home Care (MDS-HC) were used to evaluate a series of indicators suggested by international experts and by focus groups conducted in Canada and the United States. Risk adjustment methods were derived and validated using data from Ontario and Michigan. RESULTS: Of the 73 original candidate HCQIs, 22 were retained for the final list of recommended indicators. All but three indicators include risk adjusters based on individual-level covariates. An agency-level risk adjustment was developed to correct for selection and ascertainment bias. IMPLICATIONS: The HCQIs are new tools providing a first step along the path of quality improvement for home care. These indicators can provide high-quality evidence on performance at the agency level and on a regional basis.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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 it