What People Want From Their Professionals: Attitudes Toward Decision‐making Strategies
Bibliographic record
Abstract
ABSTRACT Attitudes toward four types of decision‐making strategies—clinical/fully rational, clinical/heuristic, actuarial/fully rational, and actuarial/heuristic—were examined across three studies. In Study 1, undergraduate students were split randomly between legal and medical decision‐making scenarios and asked to rate each strategy in terms of the following: (i) preference; (ii) accuracy; (iii) fairness; (iv) ethicalness; and (v) its perceived similarity to the strategies used by actual legal and medical professionals to make decisions. Studies 2 and 3 extended Study 1 by using a more relevant scenario and a community sample, respectively. Across the three studies, the clinical/fully rational strategy tended to be rated the highest across all attitudinal judgments, whereas the actuarial/heuristic strategy tended to receive the lowest ratings. Considering the two strategy‐differentiating factors separately, clinically based strategies tended to be rated higher than actuarially based strategies, and fully rational strategies were always rated higher than heuristic‐based strategies. The potential implications of the results for professionals' and those affected by their decisions are discussed. Copyright © 2011 John Wiley & Sons, Ltd.
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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 | 0.013 | 0.040 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".