What matters to patients and families: A content and process framework for clarifying preferences, concerns and values
Bibliographic record
Abstract
Background: Values clarification, or sorting out what matters to a patient or family relevant to a health decision, is a fundamental part of shared decision making. Little is known about how this process occurs. We aimed to describe how values clarification occurs in routine primary care.Methods: Using framework analysis and an established taxonomy, two independent researchers analyzed 260 consultations in five family medicine clinics across Quebec. Two questions guided our analyses: 1) What categories exist regarding what matters to patients? 2) What patterns exist in discussions of what matters to patients? Results: 1) Five distinct categories of what matters to patients were apparent: preferences, concerns, treatment-specific values, life goals or philosophies, and broader contextual or sociocultural values. Preferences and concerns were the matters most commonly raised. 2) Diverse patterns of values clarification emerged based on three analytical questions: Who initiates the discussion about what matters to patients? When? What information is discussed? The most frequent pattern was clinicians soliciting patients’ concerns and preferences during the information-gathering phase. The second most common pattern was similar, except that patients’ spontaneously raised what matters to them.Limitations: The study was descriptive and based on audio-recorded visits. We did not interview patients and clinicians to elicit their perspectives.Conclusions: There are five distinct categories of information that matters to patients as well as clear patterns of how values clarification occurs in routine primary care consultations. Clinicians could be sensitive to these categories when engaging in the process of values clarification, and may wish to pay particular attention to the opening minutes of a consultation. This study provides a structure for future identification of best practices in values clarification.
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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 | 0.076 | 0.057 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.013 | 0.007 |
| Science and technology studies | 0.017 | 0.033 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".