THE DIVERT-CARE CATALYST TRIAL: TARGETED CHRONIC-DISEASE MANAGEMENT FOR HOME CARE CLIENTS
Bibliographic record
Abstract
Home care patients are a large population of vulnerable older adults living in the community. They are medically complex, access care across settings, have very high rates emergency department use, and have relatively poor access to effective chronic disease management. We tested a multi-disciplinary intervention deployed with a case-finding tool to determine its ‘real-world’ effectiveness. A cardio-respiratory disease management intervention was developed based on existing guidelines and deployed using the validated Detection of Indicators and Vulnerabilities of Emergency Room Trips (DIVERT) Scale. Intervention components were refined and delivered by a multi-disciplinary group of geriatricians, cardiologists, primary care providers, home care coordinators, nurses, and pharmacists. Components included: sustained self-care training, patient self-care resources, medication review, advanced care planning, clinician communication tools, and staff education. We conducted a non-randomized pragmatic cluster trial. One hundred home care patients from three geographic areas were enrolled for the intervention over 6 months. The control group included patients who met the same eligibility in the six surrounding geographic areas. A city-wide control group was also included ad hoc. Data were analyzed based on intent-to-treat. The absolute risk of an emergency department visit was reduced by 20% over the 7-month follow-up. Nursing costs increased by approximately $4 per day, or approximately $500 over the entire follow-up period. Results were similar with the ad hoc control group. Targeted, multi-component cardio-respiratory disease management interventions are feasible and effective for home care clients. The trial received honours from provincial heath care organizations. A large pragmatic cluster-randomized trial is being planned.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".