| Id |
Subject |
Object |
Predicate |
Lexical cue |
| T141 |
0-4 |
Sentence |
denotes |
2.4. |
| T142 |
5-21 |
Sentence |
denotes |
Analytic Methods |
| T143 |
22-378 |
Sentence |
denotes |
In Section 3.1, scores of the COVID-19 anxiety variable were categorized into quintiles, and the quintiles were dummy-coded with the lowest one used as the reference category [20] Regression models were used to estimate the relationship between the COVID-19 anxiety quintiles and the PHQ-15 subscales (pain, gastrointestinal, cardiopulmonary, and fatigue). |
| T144 |
379-487 |
Sentence |
denotes |
Model 1 included the four dummy-coded COVID-19 anxiety variables as predictors of the four PHQ-15 subscales. |
| T145 |
488-624 |
Sentence |
denotes |
The regression coefficient for each dummy-coded variable is interpreted as the mean difference between each quintile and the lowest one. |
| T146 |
625-722 |
Sentence |
denotes |
Model 1 was also run separately with the total PHQ-15 summed scale score replacing the subscales. |
| T147 |
723-912 |
Sentence |
denotes |
In Model 2 the covariates (age, gender, income, pre-existing health problems, and GAD) were included as predictors, with the addition of Italian region mourning for COVID-19 losses factors. |
| T148 |
913-1146 |
Sentence |
denotes |
In Section 3.2, measures of depression (PHQ-9), generalized anxiety (GAD-7), trauma symptoms relating to the pandemic (ITQ) and COVID-19 anxiety were considered as dependent variables for three binary logistic regression models [19]. |
| T149 |
1147-1377 |
Sentence |
denotes |
The predictor variables were age, gender, living location, living alone, presence of children in the household, income, pre-existing health conditions in self and someone close, and perceived risk of infection over the next month. |
| T150 |
1378-1448 |
Sentence |
denotes |
Italian region and mourning for COVID-19 losses factors were included. |
