Provider, Caregiver, and Patient Experiences of an Integrated Care Program for Older Adults Designated as Alternate Level of Care: A Qualitative Case Study
Bibliographic record
Abstract
Introduction: Following hospitalization, older adults with complex health and social care needs are often deemed to need an "alternate level of care" (ALC) where care needs are misaligned with resources. Coordinated networks can implement integrated care programs for this group in home settings. Understanding the experiences of providers, caregivers, and patients will inform ongoing implementation efforts. Methods: A qualitative case study was undertaken of North York Community Access to Resources Enabling Support (NYCARES), a novel integrated care program implemented during the COVID-19 pandemic. Data collection consisted of semi-structured interviews, document analysis, and observational field notes; data were thematically analyzed. Results: Thirty-six providers, caregivers, and patients were interviewed. Three themes were developed: 1) NYCARES as a lifeline; 2) Experiences tempered by expectations and connection; and 3) The role of integrated care. Discussion: The NYCARES program was seen as valuable, but implementation posed challenges for each participant group due to varying expectations and perceived degree of connection between patients, families, and providers. Conclusions: The local coordinated network successfully implemented the NYCARES program for ALC patients despite challenges in stakeholder connections. Similar programs should formally support caregivers and forefront multidirectional communication, particularly between providers in different implementation roles.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| 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".