Advance Care Planning and Decision-Making in a Home-Based Primary Care Service in a Canadian Urban Centre
Bibliographic record
Abstract
BACKGROUND: Advance care planning (ACP) is a process that enables individuals to describe, in advance, the kind of health care they would want in the future, and has been shown to reduce hospital-based interventions at the end of life. Our goal was to describe the current state of ACP in a home-based primary care program for frail homebound older people in Vancouver, Canada. We did this by identifying four key elements that should be essential to ACP in this program: frailty stage, documentation of substitute decision-makers, and decision-making with regard to both resuscitation (i.e., do not resuscitate (DNR)) and hospitalization (i.e., do not hospitalize (DNH)). While these elements are an important part of the ACP process, they are often excluded from common practice. METHODS: This was a cross-sectional, observational study of data abstracted from 200 randomly selected patient electronic medical records between July 1 and September 30, 2017. We describe the association between demographic characteristics, comorbidities, and four key elements of ACP documentation and decision-making as documented in the clinical record using bivariate comparison, a logistic regression model and multiple logistic regression analysis. RESULTS: In 73% (n=146) of the patient records, there was no explicit documentation of frailty stage. Sixty-four per cent had documentation of a substitute decision-maker. Of those who had their preferences documented, 90.6% (n=144/159) indicated a preference for DNR, and 23.6% (n=29/123) indicated a preference for DNH. In multiple regression modeling, a diagnosis of dementia and older age were associated with documentation of a DNR preference, adjusted odds ratio (AOR) = 4.79 (95% CI 1.37, 16.71) and AOR = 1.14 (95% CI 1.05, 1.24), respectively. Older age, male sex, and English identified as the main language spoken were associated with a DNH preference. AOR = 1.17 (95% CI 1.06, 1.28), AOR = 4.19 (95% CI 1.41, 12.42), and AOR = 3.42 (95% CI 1.14, 10.20), respectively. CONCLUSIONS: Clinician documentation of some elements of ACP, such as identification of a substitute decision-maker and resuscitation status, have been widely adopted, while other elements that should be considered essential components of ACP, such as frailty staging and preferences around hospitalization, are infrequent and provide an opportunity for practice improvement initiatives. The significant association between language and ACP decisions suggests an important role for supporting cross-cultural fluency in the ACP process.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".