Integrating health and social care in the community to support a new model of Long-term Life Care at home.
Bibliographic record
Abstract
Background: Long-term care (LTC) reform was an international priority well before the COVID-19 pandemic. While strategies to promote de-institutionalization, rehabilitation, caregiver support and enhanced home and community care have varied by country in terms of implementation and success, the pandemic universally reinforced existing system-specific barriers and weaknesses. In Canada, heightened access issues and silo-ed delivery of community-based medical, functional and social care and support services contributed to increased caregiver burnout and growing residential care waitlists. Aims: This study aimed to develop an alternative model to residential LTC that would enable older adults to live, age and receive care at home long-term. The specific objectives were to: 1) describe variation in medical, functional and psychosocial ‘life care’ needs of community-dwelling older adults; 2) develop a model of needs-based care with packages to support variation in needs; and 3) to assess preliminary feasibility of the model using the Ontario, Canada (population 15 million) health care market. Approach: An exploratory, sequential, mixed methods design was applied (5). Phase 1 involved historical analysis of 2017-18 Ontario interRAI home care assessments (n=283,601) and 2018-19 Ontario service utilization data (n=115,000) to develop unique patient vignettes. Phase 2 was a 6-week modified eDelphi process with interdisciplinary home care clinicians (n=42) to develop care packages for the model, including types and dose of care and services. Six focus groups (n=67) were then conducted with older adults, caregivers and health and social care providers across Ontario to validate and refine the model. Phase 3 explored feasibility of the emerging model through comparison of the home care patient vignettes with the needs of the residential LTC population using 2017-18 Ontario interRAI data (n=115,000). Preliminary costing of the model was based on existing system per diems and direct care costs in comparable transitional care models. Results: A model of ‘Long-term Life Care’ at home (LTLifeC model) includes care packages to meet the dominant life care needs of 6 unique patient groups representing known predictors of LTC home admission: social frailty, caregiver distress, chronic disease, cognition/ behaviours, medical complexity, and geriatric syndromes. Overlap in care needs of home care and LTC populations confirms potential to shift care to the community; yet current home care clients receive six times less daily care hours on average, compared to residential LTC standards. New LTLifeC packages of home-based interdisciplinary care ranged from 3.1-8.9 hours daily, including comprehensive assessment, integrated care planning, direct care provision and community referral(s). Initial cost comparisons suggest plausible short and long-term system benefits of model adoption. Learnings: Evidence-informed decision making for sustainable home and community care as part of an integrated system of LTC requires attention to both routinely collected health information, or ‘big data’, and expertise by lived-experience. Adoption of the LTLifeC model will require decision-making to prioritize societal values for living and aging at home and in community. Next Steps: An evaluation framework will be developed to guide pilot implementation and testing of the LTLifeC model through the lens of the quadruple aim.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".