“I don’t know exactly what you’re referring to”: the challenge of values elicitation in decision making for implantable cardioverter-defibrillators
Bibliographic record
Abstract
Sandra L Carroll,1–3 Gayathri Embuldeniya,3 Jasprit Pannag,1 Krystina B Lewis,4 Jeff S Healey,2,3,5 Michael McGillion,1,2 Lehana Thabane,2,6,7 Dawn Stacey4,8 1School of Nursing, McMaster University, Hamilton, ON, Canada; 2Population Health Research Institute, Hamilton Health Sciences, Hamilton, ON, Canada; 3Hamilton Health Sciences, Hamilton, ON, Canada; 4School of Nursing, University of Ottawa, Ottawa, ON, Canada; 5Department of Medicine, McMaster University, Hamilton, ON, Canada; 6Department of Health Research Methods, Evidence and Impact, McMaster University, Hamilton, ON, Canada; 7Biostatistics Unit, St. Joseph’s Healthcare, Hamilton, ON, Canada; 8Ottawa Hospital Research Institute, Ottawa, ON, Canada Purpose: Patients’ values are a key component of patient-centered care and shared decision making in health care organizations. There is limited understanding on how patients’ values guide their health related decision making or how patients understand the concept of values during these processes. This study investigated patients’ understanding of their values in the context of considering the risks/benefits of receiving an implantable cardioverter-defibrillator (ICD). Patients and methods: A qualitative substudy was conducted within a feasibility trial with first-time ICD candidates randomized to receive a patient decision aid or usual care prior to specialist consultation. Semi-structured interviews were conducted with participants post-implantation or post-specialist consultation. Results: Sixteen patients (ten male) aged 47–87 years participated. Of these, ten (62.5%) received the patient decision aid prior to specialist consultation. Findings revealed patients were confused by the word “values” and had difficulty expressing values related to risks/benefits during ICD decision making. When probed, values were conceptualized broadly capturing other factors such as desire to live, good quality of life, family’s views, ICD information, control over decision, and medical authority. Conclusion: This study revealed the difficulty patients considering an ICD had with articulating their values in the context of an ICD health decision and highlighted the challenge to effectively elicit patients’ values within health decisions overall. It is suggested that there should be a shift away from the use of the word “values” when speaking directly to patients toward language such as “what matters to you the most” or “what is most important to you”. Keywords: values, patient preferences, patient engagement, qualitative, health decision
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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 | 0.129 | 0.354 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.016 |
| Scholarly communication | 0.019 | 0.015 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.006 | 0.015 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".