A general model of cognitive bias in human judgment and systematic review specific to forensic mental health.
Bibliographic record
Abstract
OBJECTIVE: Cognitive biases can impact experts' judgments and decisions. We offer a broad descriptive model of how bias affects human judgment. Although studies have explored the role of cognitive biases and debiasing techniques in forensic mental health, we conducted the first systematic review to identify, evaluate, and summarize the findings. HYPOTHESES: Given the exploratory nature of this review, we did not test formal hypotheses. General research questions included the proportion of studies focusing on cognitive biases and/or debiasing, the research methods applied, the cognitive biases and debiasing strategies empirically studied in the forensic context, their effects on forensic mental health decisions, and effect sizes. METHOD: A systematic search of PsycINFO and Google Scholar resulted in 22 records comprising 23 studies in the United States, Canada, Finland, Italy, the Netherlands, and the United Kingdom. We extracted data on participants, context, methods, and results. RESULTS: = 6) focused at least in part on the general perception of debiasing strategies, with three testing for specific effects (i.e., cognitive bias training, consider-the-opposite, and introspection caution), two of which yielded significant effects. CONCLUSIONS: Considerable clinical and methodological heterogeneity limited quantitative comparability. Future research could build on the existing literature to develop or adapt effective debiasing strategies in collaboration with practitioners to improve the quality of forensic mental health decisions. (PsycInfo Database Record (c) 2022 APA, all rights reserved).
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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 | 0.312 | 0.554 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.007 |
| Bibliometrics | 0.028 | 0.032 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.006 | 0.006 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".