---
title: "BHW Short Courses"
date: '4 June 2022'
output: 
  flexdashboard::flex_dashboard:
      source_code: embed
      social: menu
      
---
```{css}
body > div.navbar.navbar-inverse.navbar-fixed-top > div > div.navbar-header > span.navbar-brand {
    font-size: 30px;
    color: white;
}
```

```{r setup, include=FALSE}
library(flexdashboard)
library(knitr)
```

# Presentation 


## Column 1 {data-width=650, .tabset}

### PB
```{r, echo=FALSE, out.width="100%", fig.cap=""}
knitr::include_graphics("F:/JPGSPH Presentation Template/Presenation for Certificates Awarding Ceremony.png")
```
  
### BHW Short Courses
Bangladesh Health Watch (BHW) Short Courses 

The Centre for Professional Skills Development in Public Health (**CPSD**) at BRAC James P Grant School of Public 
Health (**JPGSPH**) has completed  3 Short Courses for BHW as follows: 

  - Responding to the Pandemic: voice, equity and governance (**Pandemic Pilot**)
  
  - Good Governance in the Health Sector (**Good Governance**)
  
  - Equity in Accessing Healthcare Services (**Health Equity**)
  
  


The participants of these short courses were from: 

  - Government
  
  - NGO/INGO
  
  - Private sector
  
  - University faculty (particularly public health)
  
  - Other professionals in the health sector and
  
  - Journalists 
  
 

### Key Features

Key Features of Short Course are:


  - Diverse Teaching and Learning (T&L) methods: case study, video, panel discussion, free listing, interactive discussion, reflections, and team work  
  - Interconnected themes with focused learning outcomes 
  - Three phases: 
    - Phase-1: Core course contents (8-14 days)
    - Phase-2: Structured follow-up of a solution project 
    - Phase-3: Successful completion and certification 
  - Shorter sessions: 2-3 hours per day in phase-1
  - Solution project: Service Delivery Improvement Project (SDIP)- required for partial fulfillment of a course
  - Highly experienced public health experts facilitate short courses 
  - Daily evaluation by course participants:
    - contents
    - T&L
    - quality of facilitation
  - 4-6 weekly Structured follow-up sessions after phase-1 until finalization of SDIP  


### Timeline 

Timeline of Completed BHW Short Courses

  - Training Needs Assessment (TNA): 1 - 25 March 2021
  
  - Validation workshop with key stakeholders: 29 May 2021
  
  - Inauguration of short course in presence of key stakeholders: 14 September 2021
  
  - Pandemic Pilot Short Course: 14 September - 2 October 2021
  
  - Good Governance Short Course: 13 - 29 December 2021
  
  - Health Equity Short Course: 14 March - 13 April 2022
  
  - Certificate Awarding Ceremony: 4 June 2022





```{r, eval=TRUE, echo=FALSE, out.width="100%", results="asis"}
library("timevis")
library("bookdown")
timeline_data <- data.frame(id = 1:7, 
                            group = c(1,1,1, 2,2,2,3),
                            content = c("Training Needs Assessment", "Validation Workshop", "Inauguration of Short Course", "Pandemic Pilot", "Good Governance", "Health Equity", "Certificate Awarding Ceremony"),                                   start = c("2021-03-01", "2021-05-29", "2021-09-14", "2021-09-14", "2021-12-13", "2022-03-14","2022-06-04" ), 
                          #end   = c("2021-03-25", NA, NA, "2021-10-02", "2021-12-30", "2022-04-13", NA), 
                          style = c("border-color: black;color: white;Background: #FF0000;", 
                                    "border-color: black;color: white;Background: #FF0000;", 
                                    "border-color: black;color: white;Background: #FF0000;", 
                                    "border-color: black;color: white;Background: #0000FF;", 
                                    "border-color: black;color: white;Background: #0000FF;",  
                                    "border-color: black;color: white;Background: #0000FF;",
                                    "border-color: black;color: white;Background: #00FF00;"))
                          
groups <- data.frame(id = c(1,2,3), content = c("Preparatory phase", "Implementation phase", "Completion phase"), style = "font-weight: bold")                            
timevis::timevis(data =timeline_data, groups = groups, fit = TRUE, zoomFactor = 0.5, options = list(stake = TRUE, orientation = "bottom", selectable = TRUE, min = "2020-08-01", max = "2022-12-01", showCurrentTime = FALSE))
```

### All Participants
Participants Completed Short Courses: 67
```{r, eval=TRUE, echo=FALSE, out.width="50%", results="asis", fig.cap ="By Gender"}
library(echarts4r)
library(echarts4r.assets)
gender = data.frame(gender=c("Male = 40", "Female = 27"), value=c(60, 40),
                    path = c('path://M18.2629891,11.7131596 L6.8091608,11.7131596 C1.6685112,11.7131596 0,13.032145 0,18.6237673 L0,34.9928467 C0,38.1719847 4.28388932,38.1719847 4.28388932,34.9928467 L4.65591984,20.0216948 L5.74941883,20.0216948 L5.74941883,61.000787 C5.74941883,65.2508314 11.5891201,65.1268798 11.5891201,61.000787 L11.9611506,37.2137775 L13.1110872,37.2137775 L13.4831177,61.000787 C13.4831177,65.1268798 19.3114787,65.2508314 19.3114787,61.000787 L19.3114787,20.0216948 L20.4162301,20.0216948 L20.7882606,34.9928467 C20.7882606,38.1719847 25.0721499,38.1719847 25.0721499,34.9928467 L25.0721499,18.6237673 C25.0721499,13.032145 23.4038145,11.7131596 18.2629891,11.7131596 M12.5361629,1.11022302e-13 C15.4784742,1.11022302e-13 17.8684539,2.38997966 17.8684539,5.33237894 C17.8684539,8.27469031 15.4784742,10.66467 12.5361629,10.66467 C9.59376358,10.66467 7.20378392,8.27469031 7.20378392,5.33237894 C7.20378392,2.38997966 9.59376358,1.11022302e-13 12.5361629,1.11022302e-13',
'path://M28.9624207,31.5315864 L24.4142575,16.4793596 C23.5227152,13.8063773 20.8817445,11.7111088 17.0107398,11.7111088 L12.112691,11.7111088 C8.24168636,11.7111088 5.60080331,13.8064652 4.70917331,16.4793596 L0.149791395,31.5315864 C-0.786976655,34.7595013 2.9373074,35.9147532 3.9192135,32.890727 L8.72689855,19.1296485 L9.2799493,19.1296485 C9.2799493,19.1296485 2.95992025,43.7750224 2.70031069,44.6924335 C2.56498417,45.1567684 2.74553639,45.4852068 3.24205501,45.4852068 L8.704461,45.4852068 L8.704461,61.6700801 C8.704461,64.9659872 13.625035,64.9659872 13.625035,61.6700801 L13.625035,45.360657 L15.5097899,45.360657 L15.4984835,61.6700801 C15.4984835,64.9659872 20.4191451,64.9659872 20.4191451,61.6700801 L20.4191451,45.4852068 L25.8814635,45.4852068 C26.3667633,45.4852068 26.5586219,45.1567684 26.4345142,44.6924335 C26.1636859,43.7750224 19.8436568,19.1296485 19.8436568,19.1296485 L20.3966199,19.1296485 L25.2043926,32.890727 C26.1862111,35.9147532 29.9105828,34.7595013 28.9625083,31.5315864 L28.9624207,31.5315864 Z M14.5617154,0 C17.4960397,0 19.8773132,2.3898427 19.8773132,5.33453001 C19.8773132,8.27930527 17.4960397,10.66906 14.5617154,10.66906 C11.6274788,10.66906 9.24611767,8.27930527 9.24611767,5.33453001 C9.24611767,2.3898427 11.6274788,0 14.5617154,0 L14.5617154,0 Z'))

  gender %>% 
  e_charts(gender) %>% 
  e_x_axis(splitLine=list(show = FALSE), 
           axisTick=list(show=FALSE),
           axisLine=list(show=FALSE),
           axisLabel= list(show=FALSE)) %>%
  e_y_axis(max=100, 
           splitLine=list(show = FALSE),
           axisTick=list(show=FALSE),
           axisLine=list(show=FALSE),
           axisLabel=list(show=FALSE)) %>%
  e_color(color = c('#69cce6','#eee')) %>%
  e_pictorial(value, symbol = path, z=10, name= 'realValue', 
              symbolBoundingData= 100, symbolClip= TRUE) %>% 
  e_pictorial(value, symbol = path, name= 'background', 
              symbolBoundingData= 100) %>% 
  e_labels(position = "bottom", offset= c(0, 10), 
           textStyle =list(fontSize= 20, fontFamily= 'Arial', 
                           fontWeight ='bold', 
                           color= '#69cce6'),
           formatter="{@[1]}% {@[0]}") %>%
  e_legend(show = FALSE) %>%
  e_theme("westeros")
```

### Pandemic Pilot
Participants Completed Pandemic Pilot Course: 17
```{r, eval=TRUE, echo=FALSE, out.width="50%", results="asis", fig.cap = "By Gender"}
library(echarts4r)
library(echarts4r.assets)
gender = data.frame(gender=c("Male = 11", "Female = 6"), value=c(65, 35),
                    path = c('path://M18.2629891,11.7131596 L6.8091608,11.7131596 C1.6685112,11.7131596 0,13.032145 0,18.6237673 L0,34.9928467 C0,38.1719847 4.28388932,38.1719847 4.28388932,34.9928467 L4.65591984,20.0216948 L5.74941883,20.0216948 L5.74941883,61.000787 C5.74941883,65.2508314 11.5891201,65.1268798 11.5891201,61.000787 L11.9611506,37.2137775 L13.1110872,37.2137775 L13.4831177,61.000787 C13.4831177,65.1268798 19.3114787,65.2508314 19.3114787,61.000787 L19.3114787,20.0216948 L20.4162301,20.0216948 L20.7882606,34.9928467 C20.7882606,38.1719847 25.0721499,38.1719847 25.0721499,34.9928467 L25.0721499,18.6237673 C25.0721499,13.032145 23.4038145,11.7131596 18.2629891,11.7131596 M12.5361629,1.11022302e-13 C15.4784742,1.11022302e-13 17.8684539,2.38997966 17.8684539,5.33237894 C17.8684539,8.27469031 15.4784742,10.66467 12.5361629,10.66467 C9.59376358,10.66467 7.20378392,8.27469031 7.20378392,5.33237894 C7.20378392,2.38997966 9.59376358,1.11022302e-13 12.5361629,1.11022302e-13',
'path://M28.9624207,31.5315864 L24.4142575,16.4793596 C23.5227152,13.8063773 20.8817445,11.7111088 17.0107398,11.7111088 L12.112691,11.7111088 C8.24168636,11.7111088 5.60080331,13.8064652 4.70917331,16.4793596 L0.149791395,31.5315864 C-0.786976655,34.7595013 2.9373074,35.9147532 3.9192135,32.890727 L8.72689855,19.1296485 L9.2799493,19.1296485 C9.2799493,19.1296485 2.95992025,43.7750224 2.70031069,44.6924335 C2.56498417,45.1567684 2.74553639,45.4852068 3.24205501,45.4852068 L8.704461,45.4852068 L8.704461,61.6700801 C8.704461,64.9659872 13.625035,64.9659872 13.625035,61.6700801 L13.625035,45.360657 L15.5097899,45.360657 L15.4984835,61.6700801 C15.4984835,64.9659872 20.4191451,64.9659872 20.4191451,61.6700801 L20.4191451,45.4852068 L25.8814635,45.4852068 C26.3667633,45.4852068 26.5586219,45.1567684 26.4345142,44.6924335 C26.1636859,43.7750224 19.8436568,19.1296485 19.8436568,19.1296485 L20.3966199,19.1296485 L25.2043926,32.890727 C26.1862111,35.9147532 29.9105828,34.7595013 28.9625083,31.5315864 L28.9624207,31.5315864 Z M14.5617154,0 C17.4960397,0 19.8773132,2.3898427 19.8773132,5.33453001 C19.8773132,8.27930527 17.4960397,10.66906 14.5617154,10.66906 C11.6274788,10.66906 9.24611767,8.27930527 9.24611767,5.33453001 C9.24611767,2.3898427 11.6274788,0 14.5617154,0 L14.5617154,0 Z'))

  gender %>% 
  e_charts(gender) %>% 
  e_x_axis(splitLine=list(show = FALSE), 
           axisTick=list(show=FALSE),
           axisLine=list(show=FALSE),
           axisLabel= list(show=FALSE)) %>%
  e_y_axis(max=100, 
           splitLine=list(show = FALSE),
           axisTick=list(show=FALSE),
           axisLine=list(show=FALSE),
           axisLabel=list(show=FALSE)) %>%
  e_color(color = c('#69cce6','#eee')) %>%
  e_pictorial(value, symbol = path, z=10, name= 'realValue', 
              symbolBoundingData= 100, symbolClip= TRUE) %>% 
  e_pictorial(value, symbol = path, name= 'background', 
              symbolBoundingData= 100) %>% 
  e_labels(position = "bottom", offset= c(0, 10), 
           textStyle =list(fontSize= 20, fontFamily= 'Arial', 
                           fontWeight ='bold', 
                           color= '#69cce6'),
           formatter="{@[1]}% {@[0]}") %>%
  e_legend(show = FALSE) %>%
  e_theme("westeros")
```
### Good Governance
Participants Completed Good Governance Course: 22
```{r, eval=TRUE, echo=FALSE, out.width="50%", results="asis", fig.cap = "By Gender"}
library(echarts4r)
library(echarts4r.assets)
gender = data.frame(gender=c("Male = 12", "Female = 10"), value=c(55, 45),
                    path = c('path://M18.2629891,11.7131596 L6.8091608,11.7131596 C1.6685112,11.7131596 0,13.032145 0,18.6237673 L0,34.9928467 C0,38.1719847 4.28388932,38.1719847 4.28388932,34.9928467 L4.65591984,20.0216948 L5.74941883,20.0216948 L5.74941883,61.000787 C5.74941883,65.2508314 11.5891201,65.1268798 11.5891201,61.000787 L11.9611506,37.2137775 L13.1110872,37.2137775 L13.4831177,61.000787 C13.4831177,65.1268798 19.3114787,65.2508314 19.3114787,61.000787 L19.3114787,20.0216948 L20.4162301,20.0216948 L20.7882606,34.9928467 C20.7882606,38.1719847 25.0721499,38.1719847 25.0721499,34.9928467 L25.0721499,18.6237673 C25.0721499,13.032145 23.4038145,11.7131596 18.2629891,11.7131596 M12.5361629,1.11022302e-13 C15.4784742,1.11022302e-13 17.8684539,2.38997966 17.8684539,5.33237894 C17.8684539,8.27469031 15.4784742,10.66467 12.5361629,10.66467 C9.59376358,10.66467 7.20378392,8.27469031 7.20378392,5.33237894 C7.20378392,2.38997966 9.59376358,1.11022302e-13 12.5361629,1.11022302e-13',
'path://M28.9624207,31.5315864 L24.4142575,16.4793596 C23.5227152,13.8063773 20.8817445,11.7111088 17.0107398,11.7111088 L12.112691,11.7111088 C8.24168636,11.7111088 5.60080331,13.8064652 4.70917331,16.4793596 L0.149791395,31.5315864 C-0.786976655,34.7595013 2.9373074,35.9147532 3.9192135,32.890727 L8.72689855,19.1296485 L9.2799493,19.1296485 C9.2799493,19.1296485 2.95992025,43.7750224 2.70031069,44.6924335 C2.56498417,45.1567684 2.74553639,45.4852068 3.24205501,45.4852068 L8.704461,45.4852068 L8.704461,61.6700801 C8.704461,64.9659872 13.625035,64.9659872 13.625035,61.6700801 L13.625035,45.360657 L15.5097899,45.360657 L15.4984835,61.6700801 C15.4984835,64.9659872 20.4191451,64.9659872 20.4191451,61.6700801 L20.4191451,45.4852068 L25.8814635,45.4852068 C26.3667633,45.4852068 26.5586219,45.1567684 26.4345142,44.6924335 C26.1636859,43.7750224 19.8436568,19.1296485 19.8436568,19.1296485 L20.3966199,19.1296485 L25.2043926,32.890727 C26.1862111,35.9147532 29.9105828,34.7595013 28.9625083,31.5315864 L28.9624207,31.5315864 Z M14.5617154,0 C17.4960397,0 19.8773132,2.3898427 19.8773132,5.33453001 C19.8773132,8.27930527 17.4960397,10.66906 14.5617154,10.66906 C11.6274788,10.66906 9.24611767,8.27930527 9.24611767,5.33453001 C9.24611767,2.3898427 11.6274788,0 14.5617154,0 L14.5617154,0 Z'))

  gender %>% 
  e_charts(gender) %>% 
  e_x_axis(splitLine=list(show = FALSE), 
           axisTick=list(show=FALSE),
           axisLine=list(show=FALSE),
           axisLabel= list(show=FALSE)) %>%
  e_y_axis(max=100, 
           splitLine=list(show = FALSE),
           axisTick=list(show=FALSE),
           axisLine=list(show=FALSE),
           axisLabel=list(show=FALSE)) %>%
  e_color(color = c('#69cce6','#eee')) %>%
  e_pictorial(value, symbol = path, z=10, name= 'realValue', 
              symbolBoundingData= 100, symbolClip= TRUE) %>% 
  e_pictorial(value, symbol = path, name= 'background', 
              symbolBoundingData= 100) %>% 
  e_labels(position = "bottom", offset= c(0, 10), 
           textStyle =list(fontSize= 20, fontFamily= 'Arial', 
                           fontWeight ='bold', 
                           color= '#69cce6'),
           formatter="{@[1]}% {@[0]}") %>%
  e_legend(show = FALSE) %>%
  e_theme("westeros")
```

### Health Equity
Participants Completed Health Equity Course: 28
```{r, eval=TRUE, echo=FALSE, out.width="50%", results="asis", fig.cap = "By Gender"}
library(echarts4r)
library(echarts4r.assets)
gender = data.frame(gender=c("Male = 17", "Female = 11"), value=c(61, 39),
                    path = c('path://M18.2629891,11.7131596 L6.8091608,11.7131596 C1.6685112,11.7131596 0,13.032145 0,18.6237673 L0,34.9928467 C0,38.1719847 4.28388932,38.1719847 4.28388932,34.9928467 L4.65591984,20.0216948 L5.74941883,20.0216948 L5.74941883,61.000787 C5.74941883,65.2508314 11.5891201,65.1268798 11.5891201,61.000787 L11.9611506,37.2137775 L13.1110872,37.2137775 L13.4831177,61.000787 C13.4831177,65.1268798 19.3114787,65.2508314 19.3114787,61.000787 L19.3114787,20.0216948 L20.4162301,20.0216948 L20.7882606,34.9928467 C20.7882606,38.1719847 25.0721499,38.1719847 25.0721499,34.9928467 L25.0721499,18.6237673 C25.0721499,13.032145 23.4038145,11.7131596 18.2629891,11.7131596 M12.5361629,1.11022302e-13 C15.4784742,1.11022302e-13 17.8684539,2.38997966 17.8684539,5.33237894 C17.8684539,8.27469031 15.4784742,10.66467 12.5361629,10.66467 C9.59376358,10.66467 7.20378392,8.27469031 7.20378392,5.33237894 C7.20378392,2.38997966 9.59376358,1.11022302e-13 12.5361629,1.11022302e-13',
'path://M28.9624207,31.5315864 L24.4142575,16.4793596 C23.5227152,13.8063773 20.8817445,11.7111088 17.0107398,11.7111088 L12.112691,11.7111088 C8.24168636,11.7111088 5.60080331,13.8064652 4.70917331,16.4793596 L0.149791395,31.5315864 C-0.786976655,34.7595013 2.9373074,35.9147532 3.9192135,32.890727 L8.72689855,19.1296485 L9.2799493,19.1296485 C9.2799493,19.1296485 2.95992025,43.7750224 2.70031069,44.6924335 C2.56498417,45.1567684 2.74553639,45.4852068 3.24205501,45.4852068 L8.704461,45.4852068 L8.704461,61.6700801 C8.704461,64.9659872 13.625035,64.9659872 13.625035,61.6700801 L13.625035,45.360657 L15.5097899,45.360657 L15.4984835,61.6700801 C15.4984835,64.9659872 20.4191451,64.9659872 20.4191451,61.6700801 L20.4191451,45.4852068 L25.8814635,45.4852068 C26.3667633,45.4852068 26.5586219,45.1567684 26.4345142,44.6924335 C26.1636859,43.7750224 19.8436568,19.1296485 19.8436568,19.1296485 L20.3966199,19.1296485 L25.2043926,32.890727 C26.1862111,35.9147532 29.9105828,34.7595013 28.9625083,31.5315864 L28.9624207,31.5315864 Z M14.5617154,0 C17.4960397,0 19.8773132,2.3898427 19.8773132,5.33453001 C19.8773132,8.27930527 17.4960397,10.66906 14.5617154,10.66906 C11.6274788,10.66906 9.24611767,8.27930527 9.24611767,5.33453001 C9.24611767,2.3898427 11.6274788,0 14.5617154,0 L14.5617154,0 Z'))

  gender %>% 
  e_charts(gender) %>% 
  e_x_axis(splitLine=list(show = FALSE), 
           axisTick=list(show=FALSE),
           axisLine=list(show=FALSE),
           axisLabel= list(show=FALSE)) %>%
  e_y_axis(max=100, 
           splitLine=list(show = FALSE),
           axisTick=list(show=FALSE),
           axisLine=list(show=FALSE),
           axisLabel=list(show=FALSE)) %>%
  e_color(color = c('#69cce6','#eee')) %>%
  e_pictorial(value, symbol = path, z=10, name= 'realValue', 
              symbolBoundingData= 100, symbolClip= TRUE) %>% 
  e_pictorial(value, symbol = path, name= 'background', 
              symbolBoundingData= 100) %>% 
  e_labels(position = "bottom", offset= c(0, 10), 
           textStyle =list(fontSize= 20, fontFamily= 'Arial', 
                           fontWeight ='bold', 
                           color= '#69cce6'),
           formatter="{@[1]}% {@[0]}") %>%
  e_legend(show = FALSE) %>%
  e_theme("westeros")
```

### Comparison by Gender
Comparing Three Short Courses by Gender

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: Proportion of female participants are highest in Good Governance (45%) followed by Health Equity (39%) and Pandemic Pilot Course (35%)"}
library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Sex=c("Male", "Female","Total","Male","Female","Total","Male","Female","Total"),
                Courses=c("Pandemic Pilot","Pandemic Pilot", "Pandemic Pilot", "Good Governance","Good Governance", "Good Governance", "Health Equity","Health Equity", "Health Equity"),
                Frequency=c(11, 6, 17, 12, 10, 22, 17, 11, 28))
hc <- df %>% 
  hchart('column', hcaes(x = 'Courses', y = 'Frequency', group = 'Sex')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```

### Comparison by Organization
Comparing Three Short Courses by Organization type
```{r, echo=FALSE, out.width="75%", fig.cap="Summary: Proportion of participants from Government Organization (GO) are highest in Good Governance (50%) followed by Pandemic Pilot (47%) and Health Equity (46%) course"}
library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Sex=c("GO","NGO","Total","GO","NGO","Total","GO","NGO","Total"),
                Courses=c("Pandemic Pilot","Pandemic Pilot", "Pandemic Pilot", "Good Governance","Good Governance", "Good Governance", "Health Equity","Health Equity", "Health Equity"),
                Frequency=c(8, 9, 17, 11, 11, 22, 13, 15, 28))
hc <- df %>% 
  hchart('column', hcaes(x = 'Courses', y = 'Frequency', group = 'Sex')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```
### Course Team
BHW Short Course Team at CPSD, JPGSPH

Dr Sabina Faiz Rashid

Dean and Professor

BRAC James P Grant School of Public Health (JPGSPH), BRAC University


Dr Rizwan Khair

Technical Advisor

BRAC James P Grant School of Public Health (JPGSPH), BRAC University &

Associate Professor, North South University, Dhaka


Mr. Kazi Hasan Imam

Lead Facilitator

BRAC James P Grant School of Public Health (JPGSPH), BRAC University
 

Dr Proloy Barua
 
Assistant Scientist &

Focal Person of BHW Short Courses

BRAC James P Grant School of Public Health (JPGSPH), BRAC University
 

Dr Kazi Haque

Assistant Scientist

BRAC James P Grant School of Public Health (JPGSPH), BRAC University
 
 
Mr. Munirul Islam
 
Senior Project Officer

BRAC James P Grant School of Public Health (JPGSPH), BRAC University
 


# More

## Column 1 {data-width=650, .tabset}

### Transitions by Gender

Transitions of Participants from Registration to Completion of Short Courses (by Gender)

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: Female participants are more likely to retain and complete (93%) the courses compared with their male counterparts (83%)"}
library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Sex=c("Male", "Female","Total","Male","Female","Total","Male","Female","Total"),
                Phases=c("Registered","Registered","Registered", "Completed without SDIP", "Completed without SDIP","Completed without SDIP", "Completed with SDIP",  "Completed with SDIP",  "Completed with SDIP"),
                Frequency=c(60, 36, 96, 48, 29, 77, 40, 27, 67))
hc <- df %>% 
  hchart('column', hcaes(x = 'Phases', y = 'Frequency', group = 'Sex')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```

### Transitions by Organization
Transitions of Participants from Registration to Completion of Short Courses (by Organization)

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: Government (GO) participants  are more likely to retain and complete the courses (94%) compared with their NGO counterparts (81%)"}
library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Organization=c("GO","NGO","Total","GO","NGO","Total","GO","NGO","Total"),
                Phases=c("Registered","Registered","Registered", "Completed without SDIP", "Completed without SDIP","Completed without SDIP", "Completed with SDIP",  "Completed with SDIP",  "Completed with SDIP"),
                Frequency=c(44, 52, 96, 34, 43, 77, 32, 35, 67))
hc <- df %>% 
  hchart('column', hcaes(x = 'Phases', y = 'Frequency', group = 'Organization')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```

### Transitions by Pandemic Pilot
Transitions of Participants from Registration to Completion of Pandemic Short Course (by Gender)

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: Female participants  are more likely to retain and complete the Pandemic Pilot course (75%) compared with their male counterparts (73%)"}

library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Sex=c("Male", "Female","Total","Male","Female","Total","Male","Female","Total"),
                Phases=c("Registered","Registered","Registered", "Completed without SDIP", "Completed without SDIP","Completed without SDIP", "Completed with SDIP",  "Completed with SDIP",  "Completed with SDIP"),
                Frequency=c(21, 11, 32, 15, 8, 23, 11, 6, 17))
hc <- df %>% 
  hchart('column', hcaes(x = 'Phases', y = 'Frequency', group = 'Sex')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```
Transitions of Participants from Registration to Completion of Pandemic Pilot Short Course (by Organization)

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: Goverment (GO) participants  are more likely to retain and complete the Pandemic Pilot course (89%) compared with their NGO counterparts (64%)"}

library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Organization=c("GO","NGO","Total","GO","NGO","Total","GO","NGO","Total"),
                Phases=c("Registered","Registered","Registered", "Completed without SDIP", "Completed without SDIP","Completed without SDIP", "Completed with SDIP",  "Completed with SDIP",  "Completed with SDIP"),
                Frequency=c(14, 18, 32, 9, 14, 23, 8, 9, 17))
hc <- df %>% 
  hchart('column', hcaes(x = 'Phases', y = 'Frequency', group = 'Organization')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```

### Transitions by Good Governance
Transitions of Participants from Registration to Completion of Good Governance Short Course (by Gender)

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: All female participants  retain and complete the Good Governance course (100%) compared with their male counterparts (86%)"}

library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Sex=c("Male", "Female","Total","Male","Female","Total","Male","Female","Total"),
                Phases=c("Registered","Registered","Registered", "Completed without SDIP", "Completed without SDIP","Completed without SDIP", "Completed with SDIP",  "Completed with SDIP",  "Completed with SDIP"),
                Frequency=c(10, 16, 26, 10, 14, 24, 10, 12, 22))
hc <- df %>% 
  hchart('column', hcaes(x = 'Phases', y = 'Frequency', group = 'Sex')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```
Transitions of Participants from Registration to Completion of Good Governance Short Course (by Organization)

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: Both GO and NGO participants equally retain and complete Good Governance Short Course"}

library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Organization=c("GO","NGO","Total","GO","NGO","Total","GO","NGO","Total"),
                Phases=c("Registered","Registered","Registered", "Completed without SDIP", "Completed without SDIP","Completed without SDIP", "Completed with SDIP",  "Completed with SDIP",  "Completed with SDIP"),
                Frequency=c(13, 13, 26, 12, 12, 24, 11, 11, 22))
hc <- df %>% 
  hchart('column', hcaes(x = 'Phases', y = 'Frequency', group = 'Organization')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```

### Transitions by Health Equity
Transitions of Participants from Registration to Completion of Health Equity Short Course (by Gender)

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: All female participants  retain and complete the Health Equity course (100%)  while this figure is 89% for male participants"}

library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Sex=c("Male", "Female","Total","Male","Female","Total","Male","Female","Total"),
                Phases=c("Registered","Registered","Registered", "Completed without SDIP", "Completed without SDIP","Completed without SDIP", "Completed with SDIP",  "Completed with SDIP",  "Completed with SDIP"),
                Frequency=c(23, 15, 38, 19, 11, 30, 17, 11, 28))
hc <- df %>% 
  hchart('column', hcaes(x = 'Phases', y = 'Frequency', group = 'Sex')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```
Transitions of Participants from Registration to Completion of Health Equity Short Course (by Organization)

```{r, echo=FALSE, out.width="75%", fig.cap="Summary: All GO participants retain and complete the Health Equity Short Course (100%) while this figure is 82% for NGO Participants"}

library("highcharter") 
options(highcharter.theme = hc_theme_smpl(tooltip = list(valueDecimals = 0)))
df <- data.frame(Organization=c("GO","NGO","Total","GO","NGO","Total","GO","NGO","Total"),
                Phases=c("Registered","Registered","Registered", "Completed without SDIP", "Completed without SDIP","Completed without SDIP", "Completed with SDIP",  "Completed with SDIP",  "Completed with SDIP"),
                Frequency=c(15, 23, 38, 13, 17, 30, 13, 15, 28))
hc <- df %>% 
  hchart('column', hcaes(x = 'Phases', y = 'Frequency', group = 'Organization')) %>%
  hc_colors(c("#0073C2FF", "#EFC000FF","#FF0000"))
hc
```

### Res. Persons: PP 
Resource persons of Pandemic Pilot Course: 21 

 
(*Alphabetical order of last name*) 
 
Ms. Samya Afrin
 
Member
 
Naripokkho, Dhaka
 
Dr. Ziauddin Ahmed
 
Professor
 
Clinical Medicine, Temple University, Pennsylvania, USA
 
Dr. Syed Masud Ahmed
 
Professor
 
Centre of Excellence for Health Systems and Universal Health Coverage (CoE-HS&UHC)

BRAC James P Grant School of Public Health (JPGSPH), BRAC University 

Dr. Be-Nazir Ahmed

Former Director

Directorate General of Health Services (DGHS) 

Ministry of Health and Family Welfare (MoHFW), Government of Bangladesh (GoB)


Ms. Bachera Aktar

Assistant Director

For Center of Excellence for Gender, Sexual for Reproductive Health to Information (CEGSRHR), BRAC James P Grant School of Public Health (JPGSPH), BRAC University, Dhaka

Dr. A S M Alamgir

Principal Scientific Officer (PSO)
 
Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka, Bangladesh

Mr. Anir Chowdhury

Policy Advisor

Access to Information (a2i), Government of Bangladesh (GoB)

Dr. Ahmed Mushtaque Raza Chowdhury
 
Professor

Clinical Population and Family Health, Department of Population and Family Health 

The School of Public Health, Columbia University, USA
 
Dr. Abu Jamil Faisel
 
Public Health Specialists and Working Group Member

Bangladesh Health Watch (BHW)

Dr. M. Mushtuq Husain

Advisor, 
 
Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka, Bangladesh

Dr Md Khairul Islam

Regional Director

WaterAid
 

Dr. Md Tariqul Islam

Director

Sajida Hospital, Sajida Foundation, Dhaka, Bangladesh

Mr. Ahmed Javed Jamal

Founder & Convenor

Sangjog, Dhaka, Bangladesh 
 
Ms. Raihana Sayeeda Kamal
 
Media and Communications

Bangladesh Health Watch (BHW)
Dhaka, Bangladesh

Dr. M H Chowdhury Lelin
 
Chairman

Health and Hope Hospital, Dhaka

Mr. Badiul Alam Majumdar
 
Vice President & Country Director
The Hunger Project, Bangladesh

Dr. Atonu Rabbani
 
Associate Professor

Department of Economics, University of Dhaka, 
and

Mushtaque Chowdhury Professor of Health and Poverty 

BRAC James P Grant School of Public Health (JPGSPH), BRAC University, Dhaka  

Dr. Md. Mustafizur Rahman

Senior Scientist

icddr,b, Dhaka, Bangladesh

Dr. Asif M. Shahan

Associate Professor
Department of Development Studies

University of Dhaka, Dhaka

Dr. Tahmina Shirin

Professor & Director

Institute of Epidemiology, Disease Control and Research (IEDCR), Dhaka 

Dr. Shahaduz Zaman

Medical Anthropologist

Sussex, UK 


### Res. Persons: GG
Resource persons of Good Governance Course: 11 
 
(*Alphabetical order of last name*) 
 
Dr. Md. Morshed Alom
 
Director
 
Bangladesh Public Administration Training Center (BPATC), Savar, Ministry of Public Administration (MoPA), Government of Bangladesh
 
Mr. Md. Ashadul Islam
 
Former Senior Secretary
 
Planning Division of the Ministry of Planning.Previously &

Health Economics Unit (HEU) of Ministry of Health and Family Welfare, Government of Bangladesh (GoB)


Mr. Nazrul Islam
 
Director
 
Former Secretary (Coordination and Reform)

Cabinet Division, Bangladesh Secretariat, Dhaka, Government of Bangladesh
  
Dr. Taufique Joarder

Vice-Chairperson 
 
Public Health Foundation, Bangladesh
 
Dr. Rizwan Khair
 
Associate Professor
 
South Asian Institute of Policy and Governance (SIPG), North South University, Dhaka, Bangladesh
 
Dr. Mohammad Asif Shahan
 
Associate Professor
 
Department of Development Studies, University of Dhaka, Bangladesh 

 
Dr. Mohammod Abdus Sabur
 
Public Health Specialist
Dhaka, Bangladesh 
 

Mr. Md. Helal Uddin
 
Former Additional Secretary (Planning)
 
Health Service Division, Ministry of Health and Family Welfare (MoHFW), Government of Bangladesh (GoB)


### Res. Persons: HE

Resource persons of Health Equity Course: 11 
 
(*Alphabetical order of last name*) 
 
Dr. Syed Abdul Hamid
 
Professor
 
Institute of Health Economics (IHE), University of Dhaka
 
Dr. Rumana Huque
 
Professor
 
Department of Economics, Faculty of Social Sciences, University of Dhaka
 
Mr. Md. Ashadul Islam
 
Former Senior Secretary
 
Planning Division of the Ministry of Planning & 

Health Economics Unit (HEU) of Ministry of Health and Family Welfare (MoHFW), Government of Bangladesh (GoB)
 
Dr. Taufique Joarder

Vice-Chairperson 
 
Public Health Foundation, Bangladesh
 
Mr. Md. Abdus Salam Khan
 
Joint Secretary (Planning)
 
Medical Education and Family Welfare Division, Ministry of Health and Family Welfare (MoHFW), Government of Bangladesh (GoB)
 
Dr. Zahidul Quayyum
 
Professor (Health Economics) and Director Research
 
Co-Director Centre of Excellence for Urban Equity and Health (CUEH)
 
BRAC James P Grant School of Public Health (JPGSPH), BRAC University
 
Dr. Mohammod Abdus Sabur
 
Public Health Specialist
Dhaka, Bangladesh 
 
Dr. Sohana Shafique
 
Deputy Project Coordinator and Facilitator
 
Urban Health Research Group, Health System and Population Studies Division, icddr,b 
 
Mr. Md. Helal Uddin
 
Former Additional Secretary (Planning)
 
Health Service Division, Ministry of Health and Family Welfare (MoHFW), Government of Bangladesh (GoB)
 

### TH
```{r, echo=FALSE, out.width="100%", fig.cap=""}
knitr::include_graphics("F:/JPGSPH Presentation Template/Thank you.png")
```