Implementation evaluation of a stepped approach to home care assessment using interRAI systems in Ontario, Canada
Bibliographic record
Abstract
In Ontario, new home care clients are screened with the interRAI Contact Assessment and only those expected to require longer-term services receive the comprehensive RAI-Home Care assessment. Although Ontario adopted this two-step approach in 2010, it is unknown whether the assessment guidelines were implemented as intended. To evaluate implementation fidelity, the purpose of this study is to compare expected to actual client profiles and care co-ordinator practice patterns. We linked interRAI CA and RAI-HC assessments and home care referrals and services data for a retrospective cohort of adult home care clients admitted in FY 2016/17. All assessments were done by trained health professionals as part of routine practice. Descriptive analyses were used to evaluate congruency between recommended and actual practice. Adjusted cause-specific hazards and logistic approaches were used to examine time to RAI-HC assessment and being a high-priority client. Of 225,989 unique home care clients admitted to the publicly funded home care program, about three-quarters of clients were assessed with the interRAI CA only (27.9% completed the Preliminary Screener only and 46.6% completed both the Preliminary Screener and Clinical Evaluation). There was substantial agreement between the skip logic and completion of the Clinical Evaluation section (Cohen's kappa = 0.67 [95% CI: 0.66-0.67]). One-quarter of clients were assessed with both the interRAI CA and RAI-HC. As expected, RAI-HC assessed clients were older, reported more health needs, and often received home care services for >6 months. Clients in higher Assessment Urgency Algorithm (AUA) levels were significantly more likely to receive a RAI-HC assessment and be assigned to a higher home care priority level; however, 28.3% of clients in the highest AUA level did not receive a RAI-HC assessment. We conclude that the use of the interRAI CA and RAI-HC balances the investment of time and resources with the information and tools to deliver high-quality, holistic, and client-centred care. The interRAI CA guides the care co-ordinator to screen every client for a broad range of possible needs and tailor further assessment to each client's unique needs. We recommend integrating the AUA into provincial assessment guidelines as well as developing a new quality indicator focused on measuring access to the home care system.
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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 | 0.028 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".