CHARACTERIZING THE USE AND DOCUMENTATION OF ADVANCED DIRECTIVES IN FOUR DIVERSE ASSISTED LIVING FACILITIES
Bibliographic record
Abstract
Advance directives (AD) are intended to help facilitate end-of-life (EOL) wishes of people and decrease decisional burden on family members. Completion of an AD has been shown to result in reduced hospitalizations and use of life-sustaining treatment and increased utilization of hospice or palliative care services at EOL. Despite an increasing number of older adults living and dying in assisted living (AL), there is limited research examining AD documentation in this setting. As part of a larger study investigating best practices for palliative and end-of-life care in AL funded by the National Institute on Aging (1R01AG017408-01A1), we reviewed facility health records 70 residents across four diverse AL facilities. Descriptive statistics were used to characterize AD use and documentation. Of these 70 residents, the majority (64%) did not have any form of AD on file, including 6 on hospice. The average age of residents in the sample was 86, most were female (66%), white (57%), and had at least a high school education (75%). The average score on the Montreal Cognitive Assessment (MoCA) was 16 out of 30, indicating moderate levels of cognitive impairment. Considering the advanced age and declining health of this population, the lack of AD documentation has serious implications for delivery of quality care at EOL. This descriptive study examines the individual-level (e.g., sociodemographic and health characteristics) and facility-level (e.g., resident profile and facility policies) factors associated with documentation of AD in these four diverse AL facilities and provides recommendations for increasing use of ADs in these settings.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".