Cognitive debiasing 2: impediments to and strategies for change
Bibliographic record
Abstract
In a companion paper, we proposed that cognitive debiasing is a skill essential in developing sound clinical reasoning to mitigate the incidence of diagnostic failure. We reviewed the origins of cognitive biases and some proposed mechanisms for how debiasing processes might work. In this paper, we first outline a general schema of how cognitive change occurs and the constraints that may apply. We review a variety of individual factors, many of them biases themselves, which may be impediments to change. We then examine the major strategies that have been developed in the social sciences and in medicine to achieve cognitive and affective debiasing, including the important concept of forcing functions. The abundance and rich variety of approaches that exist in the literature and in individual clinical domains illustrate the difficulties inherent in achieving cognitive change, and also the need for such interventions. Ongoing cognitive debiasing is arguably the most important feature of the critical thinker and the well-calibrated mind. We outline three groups of suggested interventions going forward: educational strategies, workplace strategies and forcing functions. We stress the importance of ambient and contextual influences on the quality of individual decision making and the need to address factors known to impair calibration of the decision maker. We also emphasise the importance of introducing these concepts and corollary development of training in critical thinking in the undergraduate level in medical education.
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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 | 0.023 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.015 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".