A Snapshot of Advance Directives in Long-Term Care: How Often Is "Do Not" Done?
Bibliographic record
Abstract
Advance directives allow individuals and their families or legal guardians to communicate preferences for interventions and treatments in the event that these individuals are no longer able to make decisions for themselves. This study examines how often do-not-hospitalize (DNH) and do-not-resuscitate (DNR) directives were recorded for residents in 982 reporting Canadian long-term care facilities between 2009-2010 and 2011-2012 and, to the extent possible, whether these directives were followed in acute care settings. It found that three-quarters of long-term care residents had a directive not to resuscitate and that these directives appeared to be well followed across the continuum; only 1 in 2,500 residents with a DNR received resuscitation in hospital. Fewer residents - 1 in 5 - had a directive not to hospitalize, and about 1 in 14 (7%) of these residents was admitted to hospital. The data are unable to determine whether patients or their families provided consent for these hospitalizations at the time of a decision to transfer. Close to half of hospitalizations among residents with a DNH directive were from potentially preventable causes, such as injuries or infections. Although hospital transfers from long-term care decreased over the study period, hospitalizations could be further reduced with the enhancement of palliative care services in long-term care 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".