Treatment decision aids: conceptual issues and future directions
Bibliographic record
Abstract
BACKGROUND: In the last 10 years, there has been a major growth in the development of treatment decision aids. Multiple goals have been identified for these tools. However, the rationale for and meaning of these goals at the conceptual level, the mechanisms through which decision aids are intended to achieve these goals, and value assumptions underlying the design of aids and associated values clarification exercises have often not been made explicit. OBJECTIVE: In this paper, we present ideas to help inform the future development and evaluation of decision aids. RESULTS: We suggest, (i) that the appropriateness of using any decision aid be assessed within the context of the wider decision-making encounter within which it is embedded; (ii) that goal setting activities drive measurement activities and not the other way round; (iii) that the rationale for and meaning of goals at the conceptual level, and mechanisms through which they are intended to have an impact be clearly thought through and made explicit; (iv) that value assumptions underlying both decision aids and associated values clarification exercises be communicated to patients; (v) that taxonomies developed and used to classify various types of decision aids include a section on value assumptions underlying each tool; (vi) that further debate and discussion take place on the role of explicit values clarification exercises as a component of or adjunct to treatment decision aids and the feasibility of implementing valid measures. CONCLUSION: Further debate and discussion is needed on the above issues.
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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 | 0.086 | 0.085 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.028 |
| Scholarly communication | 0.018 | 0.035 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.011 | 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".