RAI-HC as an innovative tool for future practice in home care
Bibliographic record
Abstract
This study aimed at examining the Resident Assessment Instrument-Home Care (RAI-HC) regarding its potential for a variety of researches as well as for improving quality of care. We searched Medline and PubMed database for peer-reviewed articles reporting primary data on the RAI-HC in English. Study site, objectives of the studies, and findings were abstracted. The search identified 34 articles that met the author's criteria. Nearly a half of the identified studies was conducted in Canada where the RAI-HC is officially used; therefore population based longitudinal survey is widely possible. Another nearly a half was based on a joint European study called ADHOC. There were broadly four types of studies. Firstly, the main focus was on a prevalence of particular conditions of home care clients across different care settings. Secondly, the focus was on predicting factors of either inappropriate events such as falls and nursing home admission or appropriate treatment regimen. Thirdly, the focus was on adverse consequences of clients' conditions, such as care giver burden as a possible consequence of depressed clients. Lastly, the focus was on development of algorism or protocol to prioritize long-term care placement or rehabilitation planning. Substantial studies have been done using the RAI-HC and they have provided useful scientific insights in the area of home care. Official use of the RAI-HC in home care agencies throughout could contribute to help identify and respond to health promotion and disease prevention issues in this population.
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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 | 0.047 | 0.061 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".