Navigating Value Elicitation: Insights from Palliative Care Providers
Bibliographic record
Abstract
Healthcare providers play a crucial role in developing care plans for patients at the end of their lives. As value elicitation becomes increasingly integrated into advance care planning, digital tools have been proposed to support providers. However, the development of these tools has primarily focused on facilitating communication of values from patients to providers. To address providers' specific needs and barriers, such as using values to create and manage medical care plans, we conducted an exploratory study capturing provider perspectives. Through interviews with 18 palliative care providers, we identified what they consider the typical value elicitation process, its associated challenges, and their visions for an ideal future process. To guide future research in determining priority areas for technology intervention, we synthesized our findings into a conceptual model that visualizes the tasks of providers in value elicitation. Comparing our model with previous models focused on patient and caregiver tasks, we found that provider perspectives introduce additional tasks in value elicitation that warrant attention: the creation, member-checking, and formal documentation of care plans by providers. Based on our findings, we argue that future work should focus on designing digital tools that support the provider-specific tasks of care plan creation and management to improve value elicitation.
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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 | 0.024 | 0.052 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.007 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".