Using an implementation science approach to enhance advance care planning practice: a community case study
Bibliographic record
Abstract
The process of advance care planning (ACP) can educate and prepare patients and caregivers to make better in-the-moment end of life decisions. Two nationwide studies on ACP implementation in Singapore identified gaps and barriers across various ACP practice settings, contributing to low ACP up-take and completion rates. This case study describes the key steps on how stakeholders were engaged and supported development of a quality practice guideline for ACP implementation. A knowledge exchange platform was convened through a multi-level partnership to form a workgroup tasked to translate evidence and develop quality ACP practice guidelines. Key knowledge users such as ACP implementers were engaged throughout the Knowledge-to-Action (KTA) action-cycle phase completing various associated tasks, including an e-survey conducted to prioritize barriers identified from the nationwide studies and other relevant evaluations. Prioritized barriers and their mapped Theoretical Domains Framework (TDF) were linked with relevant intervention functions and their associated implementation strategies, then contextualized into potential local applications by the workgroup. The implementation strategies were grouped into broader categories that formed domains in the practice guideline. The last action phase was engagement with ACP teams across various organizations to conduct pilot studies using implementation strategies that would facilitate quality implementation of ACP. Our quality guideline development process was supported by the KTA model that is iterative in nature with action-phases operationalized through a multi-level partnership. This is a novel approach to formulate a national quality practice guideline with lessons learned that could be applied to others pursuing similar endeavors.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".