The problem with your request is that you want to know if a category is significant by itself. That isn't a meaningful concept. Categorical variables are only significant relative to other levels. This is similar to the idea that a continuous variable is not significant at a single value; it is the change in the variable that is significant.

In the example below, I invent some data where Value has one distribution when Name is A and another distribution when Name is either B or C. When I do a regression with A as the baseline, both B and C are significant. When I change the baseline Name to B, A is significant but C is not. This does not mean that C has suddenly lost its significance. It just means that switching from B to C does not change the value of the Value variable. This was apparent in the original fit where you can see that the Estimates of B and C are very close. Both fits show, in different ways, that B and C are different from A but not from each other.

```
DF <- data.frame(Name = rep(c("A", "B", "C"), each = 50),
Value = c(rnorm(50, 0, 0.2), rnorm(100, 1, 0.5)))
summary(lm(Value ~ Name, data = DF))
#>
#> Call:
#> lm(formula = Value ~ Name, data = DF)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1.52319 -0.23338 -0.00452 0.24193 1.51990
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.01283 0.06701 0.191 0.848
#> NameB 0.97715 0.09476 10.312 <2e-16 ***
#> NameC 1.07792 0.09476 11.375 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.4738 on 147 degrees of freedom
#> Multiple R-squared: 0.5179, Adjusted R-squared: 0.5113
#> F-statistic: 78.95 on 2 and 147 DF, p-value: < 2.2e-16
DF$Name <- factor(DF$Name, levels = c("B", "C", "A"))
summary(lm(Value ~ Name, data = DF))
#>
#> Call:
#> lm(formula = Value ~ Name, data = DF)
#>
#> Residuals:
#> Min 1Q Median 3Q Max
#> -1.52319 -0.23338 -0.00452 0.24193 1.51990
#>
#> Coefficients:
#> Estimate Std. Error t value Pr(>|t|)
#> (Intercept) 0.98998 0.06701 14.775 <2e-16 ***
#> NameC 0.10077 0.09476 1.063 0.289
#> NameA -0.97715 0.09476 -10.312 <2e-16 ***
#> ---
#> Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
#>
#> Residual standard error: 0.4738 on 147 degrees of freedom
#> Multiple R-squared: 0.5179, Adjusted R-squared: 0.5113
#> F-statistic: 78.95 on 2 and 147 DF, p-value: < 2.2e-16
```

^{Created on 2022-06-11 by the reprex package (v2.0.1)}