Evidence Base of Advance Care Planning for Patients with Advanced Disease: research evidence leading to practical implementation
Bibliographic record
Abstract
The majority of chronically ill individuals do not participate in advance care planning (ACP) and therefore are denied the opportunity to clarify their values, treatment preferences and goals for end-of-life care. Numerous patient, health care provider and health system barriers to routinely facilitating effective ACP have been identified. Unlike other interventions, there are no consistent standards about when to initiate or how to conduct these discussions. In addition, patients' perspectives of the salient elements of ACP and their preferences regarding how ACP should be facilitated may differ from those of their health care professionals. Recently, however, systems and processes have been evolving to integrate ACP into routine clinical care for patients with advanced diseases, involving substantial behavioural change, health information technology, social marketing and legislation/policy changes. While data from clinical trials of multidimensional ACP interventions remain limited, preliminary evidence strongly supports the value of ACP in allowing patients to prepare for death, strengthen relationships with loved ones, achieve a sense of control, relieve burdens placed on others and through all this positively enhance hope. ACP has also been shown to strengthen patient-physician relationships, achieve higher congruence between surrogates and patients in their understanding of patients' end-of-life preferences, and attain greater satisfaction with and less conflict about these end-of-life decisions. Data specific to end-of-life care practices are more limited but suggest ACP has positive outcomes such as increased hospice length of stay, less time spent in hospital and more deaths occurring at patients' place of choice.
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 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 | 0.097 | 0.392 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.009 |
| Bibliometrics | 0.007 | 0.009 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.008 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.002 |
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".