Advancing Knowledge of Values-Clarification Processes During Complex Decision-Making Among Older Adults With Advanced Cancer: Protocol for a Pilot Randomized Trial Using Simulated Patient-Clinician Encounters
Bibliographic record
Abstract
BACKGROUND: Older adults represent the majority of individuals diagnosed with cancer in the United States and often face complex treatment decisions that require balancing survival benefits with quality-of-life considerations. Despite the emphasis on shared decision-making (SDM), many patients report that clinical guidance does not reflect their personal values. Values-clarification tools have shown slight improvements in aligning care with patient values; however, the factors by which these tools influence decision-making are less studied. OBJECTIVE: This pilot study aims to estimate the quality of values-clarification and SDM processes that occur during simulated diagnosis encounters for advanced cancer among participants who do and do not receive a values-clarification tool. METHODS: Pilot randomized study using simulated patient-clinician encounters to assess how values are elicited, processed, and integrated into treatment decisions. Participants aged 60 years and older with an advanced cancer diagnosis will be randomized to receive either a digital values-clarification instrument codeveloped through stakeholder engagement, referred to as Values and Outcomes to Improve Cancer Experiences (VOICE), or a general communication guide from the American Cancer Society (ACS). Each participant will engage in 2 simulated encounters (values-based and nonvalues-based), conducted by trained medical students portraying oncologists. The simulations are structured using a situational awareness framework. A novel rating scale, referred to as VECTORS (Values Elicitation and Clarification of Treatment Options Rating Scale), will measure the quality of rapport building, clinician engagement, and patient engagement through observation. Additional validated instruments will be used to quantify observed (OPTION-5) and patient-reported (CollaboRATE) SDM behaviors and patients' perceived usefulness of either VOICE or the ACS guide (PrepDM [Preparation for Decision-Making]). Qualitative interviews will be used to better understand participants' experiences during encounters and perceptions of VOICE or the ACS guide. RESULTS: The study was conducted between September 2024 and December 2024, with a total of 44 participants, and is ready for data analysis. CONCLUSIONS: This pilot study will provide preliminary evidence into the quality of values-clarification processes occurring during patient-clinician encounters among older adults making treatment decisions for advanced cancer. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/80531.
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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 | 0.035 | 0.053 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.062 | 0.009 |
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".