Effects of Design Features of Explicit Values Clarification Methods
Bibliographic record
Abstract
BACKGROUND: Diverse values clarification methods exist. It is important to understand which, if any, of their design features help people clarify values relevant to a health decision. PURPOSE: To explore the effects of design features of explicit values clarification methods on outcomes including decisional conflict, values congruence, and decisional regret. DATA SOURCES: MEDLINE, all EBM Reviews, CINAHL, EMBASE, Google Scholar, manual search of reference lists, and expert contacts. STUDY SELECTION: Articles were included if they described the evaluation of 1 or more explicit values clarification methods. DATA EXTRACTION: We extracted details about the evaluation, whether it was conducted in the context of actual or hypothetical decisions, and the results of the evaluation. We combined these data with data from a previous review about each values clarification method's design features. DATA SYNTHESIS: We identified 20 evaluations of values clarification methods within 19 articles. Reported outcomes were heterogeneous. Few studies reported values congruence or postdecision outcomes. The most promising design feature identified was explicitly showing people the implications of their values, for example, by displaying the extent to which each of their decision options aligns with what matters to them. LIMITATIONS: Because of the heterogeneity of outcomes, we were unable to perform a meta-analysis. Results should be interpreted with caution. CONCLUSIONS: Few values clarification methods have been evaluated experimentally. More research is needed to determine effects of different design features of values clarification methods and to establish best practices in values clarification. When feasible, evaluations should assess values congruence and postdecision measures of longer-term outcomes.
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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 | 0.335 | 0.689 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.008 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.009 | 0.011 |
| Open science | 0.004 | 0.006 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 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".