Come As You Are - Assessing the Efficacy of a Nurse Case Management HIV Prevention and Care Intervention Among Homeless Youth

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported.

An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported.\\

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

An item from the Youth Risk Behavior Survey was used to assess this outcome. The items asked if a condom was used at last sex. The number of participants who answered yes is reported.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea.

Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

Sexually Transmitted Infection tested includes syphilis, chlamydia and gonorrhea.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

The Brief Symptom Inventory 18 (BSI-18) consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

The BSI-18 consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

The BSI-18 consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

The BSI-18 consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

The BSI-18 consists of 18 items on a 5-point (0-4) Likert scale and is designed to assess current psychological distress (over the past 7 days). Total score ranges from 0 to 72, with higher scores indicating greater distress.

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

Participants will be asked if they live in a shelter, apartment/house, with someone, outside, or in a car, etc.

An item from the Texas Christian University (TCU) drug screen II was used to assess this outcome. The item listed various drug substances and asked whether any of those listed had been used in the past 30 days. The number of participants who answered yes is reported.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

The Patient Health Questionnaire (PHQ-9) total score ranges from 0 to 27, with higher scores indicating more severe depression.

Multiple imputation was used because there were high missing fractions for many variables. This approach assumed that data were missing at random (MAR) and the imputation model used the same multilevel modeling approach that was used for analysis. The models made use of the correlations among repeated measurements for participants to estimate missing values. The descriptive statistics represent averages across 10 imputed data sets.

An item from the Texas Christian University (TCU) drug screen II was used to assess this outcome. The item listed various drug substances and asked whether any of those listed had been used in the past 30 days. The number of participants who answered yes is reported.