C-SEMA: USING DATA TO OPTIMIZE A CHILD PROTECTION HELPLINE IN TANZANIA

UNDERSTANDING COUNSELOR PERFORMANCE BY ANALYZING NON-LEGITIMATE CALLS

 

BACKGROUND

C-Sema is a National Organization that provides an all-inclusive approach to child services which involves working with children, their parents, communities and local governments to understand challenges at all levels.

C-Sema is currently managing the National Child Helpline (CHL) in-collaboration with the Government of Tanzania which is a toll-free telephone line services available across all networks in Tanzania and Zanzibar by dialing 116.  Witnesses to Violence, Abuse, Neglect, and Exploitation (VANE) against children are able to make a report through the Helpline, and they are connected to C-Sema counselors who record cases in a decentralized (excel-based) database, which C-Sema shares with the government to highlight challenges and lessons learned in the provision of child services. Counselors evaluate cases as they are reported and provide advice to the caller, forward the incident to appropriate government agencies, or assign specialized C-Sema staff to follow up.

 

Problem

The toll-free nature of the National Helpline means that not all calls are actual reports of VANE. Some are prank calls; some are disconnected before the caller speaks; some are abusive calls aimed at counselors. Such calls utilize C-SEMA resources in ways not intended or designed for proper monitoring and analysis, a situation that prevents the counselors – especially newly hired ones – and the system from operating at their optimal level.

While it is not possible to block non-legitimate calls at the network level, C-SEMA wants to use the data to understand which counselors are more adept at handling these calls. This can help C-SEMA formalize  a strategy for addressing non-legitimate calls that can be disseminated to those counselors who don’t do as well. While the merits of this analysis are clear, C-SEMA lacked personnel with the analytical skills and requisite tools necessary to analyze and visualize their data. Consequently, it was difficult for C-SEMA to perform evidence-based analysis of Helpline performance and link that performance to the organization’s programmatic targets.

 

SOLUTION

Supported by dLab, C-SEMA is engaged in data analysis leading to visualizations that will serve as decision support tools linked to the Helpline. The aim is to analyze disaggregated data to obtain a high-level understanding of their counselors’ and, by extension, their system’s performance, especially the burden that is placed on both counselors and the Helpline by non-legitimate calls.

 

PROCESS

Data scientists at the dLab began with a decentralized Excel-based system that C-SEMA used to track calls from 2013 to 2016. Joining the data permitted analysis that was impossible with the decentralized records and allowed C-SEMA to evaluate the impact that non-legitimate calls were having on their Helpline. On a system-wide level, they were able to understand the relative number of legitimate calls to non-legitimate ones, the duration of calls from each of the two categories, and therefore the relative resources that each typically receives. Further, by tracking data from individual counselors, the dLab’s analysis has allowed C-SEMA to identify which counselors handle non-legitimate calls most efficiently, information that can allow the organization to begin identifying the strategies that those counselors already employ and that can be shared across the organization as best practices .

While C-SEMA tracks a number of data points for each incoming call, the dLab Data Science team identified and advised on data gaps that C-SEMA could fill in the future in order to allow for more detailed and informative analysis and further optimization of their Helpline.

 

OUTCOMES & IMPACTS

As a result of this collaboration with the dLab, C-SEMA will be able to

  • Develop a comprehensive decision support system based on the data analysis and facilitated through data visualization;
  • Map the population aided by their Helpline;
  • Develop a better understanding of how call frequency varies on a daily, weekly, and monthly basis; and
  • Improve their service by capturing secondary / derived data from the analysis such as how long people take to get acquainted with the helpline before reporting their incidences (silent & blank calls).

Concluding two sentences

 

KEY COLLABORATORS

C-SEMA implements a National Helpline service as part of its mission to protect child welfare in the Tanzanian mainland. www.sematanzania.org

M-CEMA

Tanzania Data Lab (dLab) isa national data hub to promote data innovations, literacy, data use and multi-stakeholder data collaborators.The dLab is promoting innovation and data literacy through a premier center of excellencehttp://www.dlab.or.tz

dlab

 

Needs a figure of some sort in this section to show breakdown of legit vs. non-legit calls. Number of minutes devoted to each, perhaps?

Something needed here to wrap this up

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