The vexing problem of defining the meaning, role and measurement of values in treatment decision-making
Bibliographic record
Abstract
Two international movements, evidence-based medicine (EBM) and shared decision-making (SDM) have grappled for some time with issues related to defining the meaning, role and measurement of values/preferences in their respective models of treatment decision-making. In this article, we identify and describe unresolved problems in the way that each movement addresses these issues. The starting point for this discussion is that at least two essential ingredients are needed for treatment decision-making: research information about treatment options and their potential benefits and risks; and the values/preferences of participants in the decision-making process. Both the EBM and SDM movements have encountered difficulties in defining the meaning, role and measurement of values/preferences in treatment decision-making. In the EBM model of practice, there is no clear and consistent definition of patient values/preferences and no guidance is provided on how to integrate these into an EBM model of practice. Methods advocated to measure patient values are also problematic. Within the SDM movement, patient values/preferences tend to be defined and measured in a restrictive and reductionist way as patient preferences for treatment options or attributes of options, while broader underlying value structures are ignored. In both models of practice, the meaning and expected role of physician values in decision-making are unclear. Values clarification exercises embedded in patient decision aids are suggested by SDM advocates to identify and communicate patient values/preferences for different treatment outcomes. Such exercises have the potential to impose a particular decision-making theory and/or process onto patients, which can change the way they think about and process information, potentially impeding them from making decisions that are consistent with their true values. The tasks of clarifying the meaning, role and measurement of values/preferences in treatment decision-making models such as EBM and SDM, and determining whose values ought to count are complex and difficult tasks that will not be resolved quickly. Additional conceptual thinking and research are needed to explore and clarify these issues. To date, the values component of these models remains elusive and underdeveloped.
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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 | 0.293 | 0.341 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.009 | 0.134 |
| Scholarly communication | 0.023 | 0.053 |
| Open science | 0.008 | 0.024 |
| Research integrity | 0.017 | 0.048 |
| 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".