Making the implicit explicit: A visual model for lowering the risk of implicit bias of mental/behavioural disorders on safety and quality of care
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Persons with mental illness and/or addictions have poorer health outcomes than the general population. Lower quality of healthcare has been identified as an important factor. A main contributor to lower quality of care for people with mental illnesses and/or addictions may be the cognitive implicit bias of mental versus physical care when assessing and categorizing a patient's clinical presentation. The objective of this article is to highlight how this implicit cognitive bias of mental versus physical care can result in human factor risks to quality of care. We provide three specific case examples of where these quality concerns arise. We also propose the use of a new visual tool to help educate and create awareness of this implicit-bias-based risk and quality care problem.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it