# In a regression model, how to interpret a IC 95% for R-squared with negative values but with a significant effect of a predictor?

Imagine a regression with 3 predictors and one dependent variable with a sample size over 400. Only one predictor has a positive significant effect, p value equals to .02 aprox. R-squared is low (less than .03) and the R-squared IC 95% includes negative values: I have read that this means my model has worse fit than a horizontal line. Therefore, how to interpret this result? If my model is poor, how can I explain the significant effect of one predictor?

$$H_o: \beta_1 = \beta_2 = ... = \beta_p = 0$$
$$H_a:$$ at least one beta is non-zero.