# data[].attributes.headline
What do we want to learn? What insights are valuable for us as a team/STAP Lead?

# data[].attributes.subject
What do we want to learn? What insights are valuable for us as a team/STAP Lead?

# data[].attributes.headline
How do we define a good quality STAP? How do we know the STAP has met its purpose? What criteria should we use to monitor the STAP?

# data[].attributes.subject
How do we define a good quality STAP? How do we know the STAP has met its purpose? What criteria should we use to monitor the STAP?

# data[].attributes.headline
How do we link this to IP feedback and IP TA monitoring? How do we feed challenges by IPs back into our STAPs?

# data[].attributes.subject
How do we link this to IP feedback and IP TA monitoring? How do we feed challenges by IPs back into our STAPs?

# data[].attributes.headline
How do you envision this monitoring to take place? 

# data[].attributes.subject
How do you envision this monitoring to take place? 

# data[].attributes.headline
to ensure that the TA is effective and valuable

# data[].attributes.subject
to ensure that the TA is effective and valuable

# data[].attributes.headline
to ensure TA has an impact, leads to the desired outcomes

# data[].attributes.subject
to ensure TA has an impact, leads to the desired outcomes

# data[].attributes.headline
IP sees progress in their project on STAP topic, experiences learnings 

# data[].attributes.subject
IP sees progress in their project on STAP topic, experiences learnings 

# data[].attributes.headline
to make sure it is meeting the needs of IPs, adding to the value of CFYE, ensuring the achievement of job targets

# data[].attributes.subject
to make sure it is meeting the needs of IPs, adding to the value of CFYE, ensuring the achievement of job targets

# data[].attributes.headline
how effective it has been in terms of meeting job targets, or driving impact, as well as how the STAP can be linked back to fund-level learning and innovation

# data[].attributes.subject
how effective it has been in terms of meeting job targets, or driving impact, as well as how the STAP can be linked back to fund-level learning and innovation

# data[].attributes.headline
compare how they rate themselves on certain topics before and after TA support

# data[].attributes.subject
compare how they rate themselves on certain topics before and after TA support

# data[].attributes.headline
to understand and track the use by IPs (since it's new)

# data[].attributes.subject
to understand and track the use by IPs (since it's new)

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at the beginning, more frequently - once a quarter maybe, or every two months

# data[].attributes.subject
at the beginning, more frequently - once a quarter maybe, or every two months

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on fund level seeing improvement regarding certain topics

# data[].attributes.subject
on fund level seeing improvement regarding certain topics

# data[].attributes.headline
I think there are a lot of surveys typically and these don't always generate rich responses - maybe something like a workshop once or twice a year that is interactive and uses different assessment probing can help us get better ideas. 

# data[].attributes.subject
I think there are a lot of surveys typically and these don't always generate rich responses - maybe something like a workshop once or twice a year that is interactive and uses different assessment probing can help us get better ideas. 

# data[].attributes.headline
after each assignment a (brief) evaluation to know whether or not to continue with STAP provider?

# data[].attributes.subject
after each assignment a (brief) evaluation to know whether or not to continue with STAP provider?

# data[].attributes.headline
to understand differences in regional applicability/difference of relevance/usefulness per region or per type of IP/user

# data[].attributes.subject
to understand differences in regional applicability/difference of relevance/usefulness per region or per type of IP/user

# data[].attributes.headline
not too often - we would really have to think about how to make this useful for us but not a big burden for the IPs or countryleads. Max biannually 

# data[].attributes.subject
not too often - we would really have to think about how to make this useful for us but not a big burden for the IPs or countryleads. Max biannually 

# data[].attributes.headline
need to know some baseline, then mid-progress and at the end

# data[].attributes.subject
need to know some baseline, then mid-progress and at the end

# data[].attributes.headline
not too often (we are already monitoring so many different things)  every 6-12 months, not more often)

# data[].attributes.subject
not too often (we are already monitoring so many different things)  every 6-12 months, not more often)

# data[].attributes.headline
to get impact stories, to showcase and share with other IPs

# data[].attributes.subject
to get impact stories, to showcase and share with other IPs

# data[].attributes.headline
self evaluation by IPs, evaluation by TA provider

# data[].attributes.subject
self evaluation by IPs, evaluation by TA provider

# data[].attributes.headline
linking to QPRS (to not overburden IPs) making it a question in the structure of the QPR w the IP)

# data[].attributes.subject
linking to QPRS (to not overburden IPs) making it a question in the structure of the QPR w the IP)

# data[].attributes.headline
use results of TA satisfaction survey as well to compare/complement

# data[].attributes.subject
use results of TA satisfaction survey as well to compare/complement

# data[].attributes.headline
through reflection sessions. though it might be very time consuming for IPs 

# data[].attributes.subject
through reflection sessions. though it might be very time consuming for IPs 