In our workflow, we create all our pre-loaded assignments at allot them to a supervisor. They will then allot these assignments to the interviewers based on the pre-loaded data (location, gender etc).
The problem here is that the assignments can’t be filtered by these pre-loaded variables. The search searches across variables and is often not ideal (Search for the term Anji will return an assignment where Village is Anji or Name is Ranjit and so on).
We would also like to filter out assignments where the expected interviews are collected (Expected = Collected; Expected < Collected)
Or as an alternative, we could have an interface to re-assign assignments with set parameters (eg, Assign assignments with village = villagex and sex = male to interviewer_x.)
This should only effect the HQ.
This is would be a feature for the HQ/Admin
We can use this in our upcoming round of the HDSS. We can also use this in the baseline data collection of our community based trial on prevention of NCDs.
For larger surveys, we resort to creating a shiny app that makes this easier for the staff to use.
The app fetches the assignments for the survey questionnaire, filters assignments that have achieved the expected interviews, joins this with the pre-loaded data. New the assignments with the required variables are filtered and assigned to the appropriate interviewer.
Problem with this is that it takes a long time to download all the assignments to the shiny app. Rarely, this data could be stale as someone may have completed and synced an interview after the assignment data was fetched.
This will speed up the re-assignment process significantly and also improve the accuracy of the process. In our surveys we have seen errors in assigning assignments due to manual errors.
This feature will not effect the existing interviews, assignments or data.
Any survey that has large number of pre-loaded assignments and multiple interviewers should find this feature very useful.
This may not be of any use when the assignments that don’t have any pre-loaded data.