Conditional logit analysis regression results for total sample
| Attribute | Level | Effects codeda | Dummy codeda | Willingness to wait for an appointment (weeks) |
|---|---|---|---|---|
| Coefficient (95% CI) | Coefficient (95% CI) | MRS | ||
| Length of consultation | (minutes) | 0.001 (−0.007 to 0.008)b | 0.001 (−0.007 to 0.008)b | |
| Waiting time | (weeks) | −0.540 (−0.584 to −0.496) | −0.540 (−0.584 to −0.496) | |
| Convenience/availability | Normal working hours only | – | – | |
| Any time | 0.041 (−0.003 to 0.085)b | 0.083 (−0.002 to 0.167)b | ||
| Healthcare professional | Any GP | – | – | |
| GP of your choice | 0.244 (0.204 to 0.283) | 0.487 (0.403 to 0.571) | 0.902 = (−0.487/−0.540) | |
| Listening to you | Very poor | – | – | |
| Poor | −0.678 (−0.781 to −0.575) | 0.359 (0.206 to 0.511) | 0.665 = (−0.359/−0.540) | |
| Good | 0.845 (0.758 to 0.933) | 1.882 (1.739 to 2.025) | 3.485 = (−1.882/−0.540) | |
| Very good | 0.870 (0.773 to 0.966) | 1.906 (1.728 to 2.085) | 3.530 = (−1.906/−0.540) | |
↵a In the case of effects coding the coefficients show the effect of each variable relative to the grand mean. With dummy coding of categorical variables, the coefficients show the effect relative to the omitted category.
↵b Coefficient not significantly different from zero; all other coefficients significant at P<0.001. MRS = marginal rate of substitution.
Number of observations = 601. Pseudo-R2 = 0.4283.