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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".