Exploring Subconscious Bias
Bibliographic record
Abstract
Background: Implicit biases involve subconscious associations that lead to a negative evaluation of a person based on irrelevant characteristics such as race or gender. This audit of management of patients who missed appointments investigates the presence of implicit bias in our unit. Methods: We retrospectively analysed discharge rates in 285 patients who missed an outpatient appointment between from 1/4/2020 at Guy’s and St Thomas’ Hospital. 285 patients were categorised into predefined ethnic categories: White British (WB) vs Black, Asian and Minority Ethnic (BAME) vs Other White (OW) after reading the patient’s names. In the same fashion, we also assigned gender. Results: We did not find differences in discharge rates among self-reported ethnic and gender groups. Patients with WB sounding names were more likely to be discharged when compared to patients with BAME sounding names (35% vs 58%). Discharge rates between males and females did not differ. Conclusion: Our results suggest that implicit bias may play a role in decision-making whether to rebook a patient after missing an appointment.
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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 | 0.017 | 0.117 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".