Table 2.
The association of race, insurance status, and gender with ChatGPT responses being tailored to the same social factor.
| Patient characteristic | Social factor response takes into consideration | |||||
|---|---|---|---|---|---|---|
| Race OR (95% CI) |
P-value | Insurance status OR (95% CI) |
P-value | Gender OR (95% CI) |
P-value | |
| Race | 3.93 (0.89–27.40) | 0.10 | 1.85 (0.81–4.35) | 0.14 | 1.88 (0.76–4.62) | 0.18 |
| Insurance status | 0.78 (0.18–3.14) | 0.73 | 9.76 (3.79–28.1) | < 0.001 | 1.22 (0.51–2.99) | 0.65 |
| Gender | 0.78 (0.18–3.14) | 0.73 | 0.77 (0.33–1.75) | 0.53 | 0.82 (0.33–1.97) | 0.65 |
We used simple logistic regression to estimate the association between social factors mentioned in a vignette and a tailored response to that factor. Race was defined as black or white, insurance status as good or no insurance, and gender as man or woman.