Advance care planning in general practice and beyond : a cluster-randomized controlled trial of the ACP-GP intervention
Bibliographic record
Abstract
People with a chronic, life-limiting illness may be confronted with complex choices about their medical care. Their illness may also lead to a loss of decisional capacity. Advance care planning (ACP) is a process of communication between the patient, healthcare professionals, and loved ones, about the patient’s values, wishes, and goals for care. By discussing these topics in a timely manner and revisiting them over time, all involved parties can be better prepared for medical decision-making in the moment and in the future. Outpatient settings, such as general practice and primary care, have been proposed as ideal for initiating and facilitating ACP. However, deficits remain in ACP in this setting, including in Belgian general practice. To mitigate barriers and maximize facilitators to ACP in Belgian general practice, a complex intervention, the ACP-GP intervention, has been developed. In the first part of this dissertation, the intervention is implemented and evaluated through a cluster-randomized controlled trial with a parallel process evaluation. The second part of this dissertation complements the first by describing insights into the implementation of ACP interventions internationally. This is accomplished through a scoping review of the literature and a qualitative study of clinicians’ experiences implementing an ACP pathway in two Canadian provinces. The findings from this dissertation lead to several recommendations for practice, policy, and future research directions. Julie Stevens has a background in Clinical Psychology. She is a researcher at the End-of-Life Care Research Group (Vrije Universiteit Brussel – Ghent University).
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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 | 0.010 | 0.016 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 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".