Exploiting order effects to improve the quality of decisions
Bibliographic record
Abstract
OBJECTIVE: To examine the effect of ordering information in a patient decision aid (PtDA) about treatments for obstructive sleep apnea (OSA). METHODS: We recruited 643 individuals to imagine that they had been diagnosed with OSA and to choose between treatment options. A value clarification exercise was used to determine which attributes of treatment mattered most to each individual. Before deciding on their preferred treatment option, we randomly assigned participants to view information with attributes in: a pre-specified order (Group 1), order of what mattered most last (Group 2), and first (Group 3). RESULTS: Of the 510 participants who provided usable results, viewing information that mattered most first was associated with choosing the treatment option most concordant with their informed values. The order effect was most pronounced in younger individuals. CONCLUSIONS: In this study of hypothetical patients, order effects were found to improve the information patients focussed on, potentially improving the quality of their decisions. PRACTICE IMPLICATIONS: The order of information presented in a PtDA can inadvertently influence patients' choices. By tailoring information order for each patient, developers cannot only overcome this dilemma, but also make it simpler for patients to choose the option that is best for them.
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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 | 0.042 | 0.256 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".