1. Overall
The standards outlined below apply to the overall stage of the programme cycle.
Operational Standards
Terminology is used consistently across programme related documentation and reporting.
Key actions
- Use terminology related to cash, vouchers and markets consistently across the programme and in accordance with the CARE Emergency Toolkit
- CALP glossary (English); (French); (Spanish); (Arabic)
- this captures the ELAN glossary content and additional content on market)
- CALP glossary (English); (French); (Spanish); (Arabic)
Operational standards
The design, implementation and monitoring of CVA is actively coordinated through strategic, technical and operational fora.
Key actions
- Participate with all coordination and technical actors (CWG, governement line ministries, relevant clusters, etc) to actively coordinate the program (at all levels i.e. national/subnational) and actively promote gender sensitivity as a recognized feature in CVA. For SRHR programming: Also participate in SRHR coordination mechanisms and highlight the needs and gaps in support of SRHR that could be coered by interventions with CVA.
- Tools/Templates
- CALP – Cash Coordination tip sheet: Examples of Cash Working Group ToR (English); (French); (Spanish); (Arabic)
- IFRC/ICRC Cash in Emergencies Toolkit – CTP 4Ws matrix template (English); (French); (Spanish); (Arabic)
- Specific gender/GBV Tools
- CARE/ UNFPA Coordination Tip Sheet – Your Role as a GBV Coordinator (English); (French); (Spanish)
- Tools/Templates
- Ensure that advocacy and communication activities relevant to CVA include reference to gender analysis and its importance in ensuring effective and gender sensitive responses. For SRHR programming : Include market-based approaches as part of SRHR response analysis and emergency preparedness plans. Highlight the needs and gaps in support of SRHR that could be covered by interventions with CVA and advocate for the inclsuion of SRHR needs in MEBs.
- Triangulate data collected using different methods and from different sources in order to identify unreliable data and inconsistencies.