Implicit gender bias in the diagnosis and treatment of type 2 diabetes: A randomized online study
Bibliographic record
Abstract
AIMS: Implicit gender biases (IGBs) are unconscious evaluations about a person based on gender. IGBs of healthcare providers may affect medical decision making. This study investigated whether IGBs and genders of patients and general practitioners (GPs) influence diagnostics and treatment decisions in the context of diabetes type 2. METHODS: Ninety-nine GPs participated in this randomized online study. Implicit Associations Tasks were used to measure two IGBs, related to lifestyle (women have a healthier lifestyle than men) and communication (men are less communicative than women). Clinical decisions regarding type 2 diabetes were measured with vignettes that included a fictional male or female patient case. RESULTS: Female GPs exhibited a significant lifestyle IGB (p < 0.001). GPs of both genders exhibited a significant communication IGB (p < 0.001). Several associations between IGBs and clinical decisions were found. The gender of the vignette character affected several outcomes, for example GPs were less certain in the diabetes diagnosis when the character was a woman (p < 0.001). CONCLUSION: We demonstrated that GPs have IGBs and these biases as well as patient's gender affect decisions of GP's when they are solving a diabetes vignette case. Future research is needed to understand the most important consequences of IGBs in the context of type 2 diabetes.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".