Trust in deliberation: The consequences of deliberative decision strategies for medical decisions.
Bibliographic record
Abstract
OBJECTIVE: Decision aids (DAs) play an increasingly critical role in supporting patients in making preference-sensitive treatment decisions. One largely untested assumption of DA design is that patients should be encouraged to deliberate carefully about their options after being informed of those options. The purpose of the present research is to test the impact of deliberative versus intuitive decision strategies in medical decision contexts. METHOD: In 3 experiments, participants were randomly assigned to make a hypothetical medical decision either intuitively, or using various deliberative strategies. In Study 1, we predicted that deliberation would improve decision confidence while not changing decisions. In Study 2, our aim was to establish whether the observed increase in confidence was due to decision-making effort, confirmation bias, or both. In Study 3, it was predicted that deliberation would cause participants to become more confident in suboptimal decisions. RESULTS: Across 3 studies, participants who deliberated felt better about their decisions and decision process, even when the decision was the same as what would have been chosen intuitively (Studies 1 and 2), and even when the decision was normatively bad (Study 3). Study 2 additionally indicated that participants' confidence was driven by confirmation bias rather than effort justification. CONCLUSIONS: Deliberative tasks may often fail to be an effective debiasing tool, and components of patient decision aids that ask patients to deliberate may serve to improve how patients feel without improving the quality of their decisions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.035 | 0.238 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".