Advance care planning in stroke: influence of time on engagement in the process
Bibliographic record
Abstract
PURPOSE: Individuals who experience stroke have a higher likelihood of subsequent stroke events, making it imperative to plan for future medical care. In the event of a further serious health event, engaging in the process of advanced care planning (ACP) can help family members and health care professionals (HCPs) make medical decisions for individuals who have lost the capacity to do so. Few studies have explored the views and experiences of patients with stroke about discussing their wishes and preferences for future medical events, and the extent to which stroke HCPs engage in conversations around planning for such events. In this study, we sought to understand how the process of ACP unfolded between HCPs and patients post-stroke. PATIENTS AND METHODS: Using grounded theory (GT) methodology, we engaged in direct observation of HCP and patient interactions on an acute stroke unit and two stroke rehabilitation units. Using semi-structured interviews, 14 patients and four HCPs were interviewed directly about the ACP process. RESULTS: We found that open and continual ACP conversations were not taking place, patients experienced an apparent lack of urgency to engage in ACP, and HCPs were uncomfortable initiating ACP conversations due to the sensitive nature of the topic. CONCLUSION: In this study, we identified lack of engagement in ACP post-stroke, attributable to patient and HCP factors. This encourages us to look further into the process of ACP in order to develop open communication between the patient with stroke, their families, and stroke HCPs.
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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 | 0.063 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".