“There Should Be A Nurse On Call”: Complex Care Needs of Low-Income Older Adults in Medicaid-Supported Assisted Living
Bibliographic record
Abstract
Background:In the United States, nearly 1 million older adults reside in assisted living facilities, which aim to provide support for safe, autonomous living. However, low-income residents, especially those in Medicaid-supported facilities, experience unmet medical and social complex care needs and limited serious illness communication due to limited resources. Objective:The objective of this study was to explore the complex care needs and serious illness communication challenges experienced by low-income older adults in Medicaid-supported assisted living. Methods:A qualitative secondary framework analysis was conducted on data from a parent qualitative study involving 17 residents aged 60 and older with serious illnesses at a Medicaid-supported facility in New York City. Residents completed the Edmonton Symptom Assessment Scale and participated in semistructured interviews. This study was guided by the National Consensus Project for Quality Palliative Care, focusing on the residents’ experiences, complex care needs, and communication within palliative care domains. Results:Residents were predominantly Black and Hispanic, with nearly one-third having a history of homelessness or shelter use, and they experienced a high symptom burden. Four key themes emerged: (1) compromised quality of life; (2) high symptom burden and limited access to care, with residents reporting pain, fatigue, and emotional distress; (3) communication gaps while navigating health care, resulting in frustration and feelings of being unheard; and (4) fragmented care coordination, which exacerbated feelings of isolation and mistrust in the health care system. Conclusion:The findings reveal that Medicaid-supported assisted living residents encounter substantial challenges related to complex care needs and serious illness communication. There is an urgent need for community-based interventions to enhance care access, improve symptom management, and facilitate effective communication, ultimately supporting the residents’ quality of life and health outcomes. Enhanced training for staff and policy changes are key to addressing these systemic barriers to care.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".