An IDEA: Safety Training to Improve Critical Thinking by Individuals and Teams
Bibliographic record
Abstract
Errors in thinking contribute to harm, delays in diagnosis, incorrect treatments, or failures to recognize clinical changes. Models of cognition are useful in understanding error occurrence and avoidance. Intra-team conflict can represent failures in joint cognitive processing. The authors developed training focused on recognizing and managing cognitive bias and resolving conflicts. The program provides context and introduces models of cognition, concepts of bias, team cognition, conflict resolution, and 2 tools. "IDEA" incorporates 4 de-biasing strategies: Identify assumptions; Don't assume correctness; Explore expectations; Assess alternatives. "TLA" presents strategies for resolving conflicts: Tell your thoughts; Listen actively, and Ask questions. A total of 4941 care providers participated in training using didactic presentations, group discussion, and simulation. Learners rated training effectiveness at 4.68 on a scale of 1 to 5 (5 as optimum) and perceived improvement in recognizing or managing errors. Nonphysician caregivers reported greatest improvement. Training to improve critical thinking is feasible, well received, and effective.
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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".