The advance directive prevalence in long‐term care: A comparison of relationships between a nurse practitioner healthcare model and a traditional healthcare model
Bibliographic record
Abstract
PURPOSE: The purpose of this study was to examine rates of completion of advance directives (ADs) among institutionalized older adults in three geographically diverse areas of the country--Arizona, Georgia, and Massachusetts. Comparisons among four variables--gender, race, education, and type of healthcare model (Evercare vs. non-Evercare), related to AD completion rates were examined. DATA SOURCES: This study was a secondary data analysis using deidentified data from 11,775 older adults enrolled in the Evercare healthcare model to 91,443 non-Evercare older adults (Minimum Data Set) during the last quarter of 2004. Chi-square analysis was used to examine any differences in gender, race, education, and healthcare model associated with the completion rates of ADs. CONCLUSIONS: The Evercare healthcare model that used nurse practitioners (NPs) consistently had significantly higher (p < .001) completion rates of ADs compared to the non-Evercare healthcare model that did not use NPs. Black people and white people in the Evercare healthcare model had similar rates of AD completion (p > .001), which is contrary to previous findings where black people had a lower completion rate. Males and females in the Evercare healthcare model had similar rates of AD completion (p > .001), which is also contrary to previous findings where females had a higher completion rate. Finally, older adults with a high school education or less and older adults with greater than a high school education in the Evercare healthcare model had similar rates of AD completion (p > .001), which is contrary to previous findings where individuals with increased education had a higher completion rate. IMPLICATIONS FOR PRACTICE: With the increasing number of older adults in the general and the long-term care population, older adults should be encouraged to complete their ADs when discussing their medical decisions with their healthcare providers. Through the use of the Evercare healthcare model, NPs are well prepared to assist their clients and families in identifying these decisions. As a result, a significantly greater proportion of ADs have been completed by individuals enrolled in the Evercare healthcare model when compared to non-Evercare individuals living in long-term care settings. By using this model, Evercare NPs ensure that the specific medical choices of their patients are carried out.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".