Factors Associated with Having a Will, Power of Attorney, and Advanced Healthcare Directive in Patients Presenting to a Rural and Remote Memory Clinic
Bibliographic record
Abstract
BACKGROUND: A Will, Power of Attorney, and Advanced Healthcare Directive are critical to guide decision-making in patients with dementia. We identified characteristics that are associated with the existence of these documents in patients who presented to a rural and remote memory clinic (RRMC). METHODS: Ninety-five consecutive patients were included in this study. Patients and caregivers completed questionnaires on initial presentation to the RRMC and patients were asked if they had legal documents. Patients also completed neuropsychological testing. Statistical analysis (t-test and χ2 test) was performed to identify significant variables. RESULTS: Seventy (73.7%) patients had a Will, 62 (65.3%) had a Power of Attorney, and 21 (22.1%) had an Advanced Healthcare Directive. Having a Will was associated with good quality of life (p = 0.001), living alone or with a spouse or partner only (p = 0.034), poor verbal fluency (p = 0.055), and European ethnicity (p = 0.028). Factors associated with having a Power of Attorney included good quality of life (p = 0.031), living alone or with a spouse or partner only (p = 0.053), and poor verbal fluency (p = 0.015). Old age (p = 0.015), poor verbal fluency (p = 0.023), and greater severity of cognitive and functional impairment (p = 0.023) were associated with having an Advanced Healthcare Directive. CONCLUSIONS: Our results indicate that poor quality of life, good performance on verbal fluency, Indigenous ethnicity, and living with others are associated with a lower likelihood of legal documents in patients with dementia. These factors can help physicians identify patients at risk of leaving their legal affairs unattended to. Physicians should discuss the creation of legal documents early on in patients with signs of dementia.
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 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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".