Development of an Advance Care Planning Policy within an Evidenced-Based Evaluation Framework
Bibliographic record
Abstract
Background: As the population is aging and medical advancements enable people to live longer, advance care planning (ACP) becomes increasingly important in guiding future care decisions; however, they are often incomplete or absent from the patient chart. This study describes the development and implementation of an ACP policy in a post-acute care and long-term care setting using a systematic implementation framework. Methods: A process evaluation that parallels the Replicating Effective Programs (REP) framework was used to understand stakeholder experiences with ACP and identify gaps in practice. Physicians, multidisciplinary staff, patients, and substitute decision makers engaged in focus groups and interviews, and completed surveys. A retrospective chart review determined Plan for Life Sustaining Treatment (PLST) form completion rates. Results: Stakeholder feedback identified barriers and facilitators to ACP including a need for staff training, user-friendly resources, and standardization of ACP practice. The PLST form was developed and embedded in the electronic medical record, and had a 92% and an 87% PLST completion rate on 2 pilot units. Conclusion: The study showed the usefulness of the REP model in guiding the evaluation as an effective tool to enhance implementation practices and inform ACP policy development that can be replicated in other organizations.
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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 | 0.603 | 0.401 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.008 | 0.009 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.007 | 0.012 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".