Table 5.
The summary of binary logistic regression analysis to predict appointment attendance rate
| Characteristics | B | S.E. | Wald | Sig | Exp(B) | 95% C.I. for | |
|---|---|---|---|---|---|---|---|
| Lower | Upper | ||||||
| Age | 0.008 | 0.008 | 0.924 | 0.337 | 1.008 | 0.992 | 1.024 |
| Gender | -0.151 | 0.243 | 0.388 | 0.533 | 0.860 | 0.534 | 1.383 |
| Marital status | 0.099 | 0.317 | 0.097 | 0.756 | 1.104 | 0.593 | 2.053 |
| Religion | -0.438 | 0.306 | 2.046 | 0.153 | 0.646 | 0.354 | 1.176 |
| Family size | 0.243 | 0.232 | 1.103 | 0.294 | 1.275 | 0.810 | 2.008 |
| Duration of illness | 0.991 | 0.243 | 16.569 | ‹0.001 | 2.693 | 1.67 | 4.338 |
| Timeline (acute/chronic) | -1.022 | 0.199 | 26.381 | ‹0.001 | 0.360 | 0.244 | 0.532 |
| Personal control | 0.850 | 0.236 | 12.950 | 0.001 | 2.340 | 1.473 | 3.718 |
| Illness coherence | 0.458 | 0.215 | 4.520 | 0.034 | 1.580 | 1.036 | 2.409 |
Nagelkerke R2=0 227; Log Likelihood=484.296; Chi-Square=7.213; Sig=0.514.