Assessing Health and Wellbeing in Skilled Nursing Facilities for Individuals with Alcohol Use Disorder
Bibliographic record
Abstract
Objectives: This study explored social group membership, experiences with alcohol use disorder (AUD), barriers to care as linked to social determinants of health, and functional limitations in Skilled Nursing Facilities (SNFs) in the United States. By leveraging comprehensive data derived from the electronic health records of SNF residents, we provide a detailed analysis of how activities of daily living (ADLs) vary across groups with different chronic and acute health conditions, specifically focusing on individuals with AUD. Design: Cross-sectional, observational study. Setting and Participants: This study relies PointClickCare Life Sciences commercially available deidentified and expert-determined data derived from SNF resident electronic health records (EHR) collected between January 1, 2015, and April 30, 2022. Methods: The study relies on a comparative analysis of ADL outcomes between 196,095 residents with AUD and the broader SNF population of 2,739,470 residents. Central outcome variables include measures of activities of daily living (ADLs) based on EHR data captured by Section G, Function, of the CMS MDS tool. Results: Findings indicate significant gender, age, and race differences in how different individuals experience functional limitations and improvements with those over time. Compared to the general SNF population, residents with AUD generally have a shorter length of stay and fewer conditions on average, but they take as many or more medications and experience less change in ADLs. Conclusions and Implications: These findings underscore the importance of considering the unique characteristics and needs of residents with AUD in SNFs. Tailored interventions and care plans that address gender, racial, and age-related differences, barriers to care, and the complexity of medication management are crucial for improving outcomes for this population. Addressing social barriers to care and ensuring equitable access to resources and support can help mitigate the disparities observed in the study.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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".