Belonging cannot be reduced to a single metric. But a social project needs to measure enough to learn, report back and improve. The way forward is to combine simple indicators, privacy and methodological honesty: attendance, return visits, the range of connections formed, participation across different strands, and optional self-reporting — always without exposing personal stories.
The risk of measuring badly
When a project works with young people, impact should never become an emotional spectacle. Powerful stories can draw attention, but they can also expose vulnerabilities. Nós Somos should share what it learns without turning people into public proof.
Useful indicators
Some indicators help without intruding: number of sessions held, aggregated attendance, 30-day return rate, activities attended, resources used, volunteers involved, barriers identified and improvements made.
This data shows how things are working and whether there is continuity. It doesn't say everything about someone's life, but it helps show whether the project is creating real opportunities to connect.
Optional self-reporting
When someone wants to share how they felt or what changed for them, that should be voluntary, authorised and used with care. Such an account should never promise that the same experience will happen for everyone. It should be presented as one person's experience, not as a guarantee of results.
Methodological limits
Small samples, a young project and a variable schedule all call for caution. In the early months, the most honest data may simply be lessons: what worked, what was missing, what barrier came up, and what will be adjusted.
Saying “we're still measuring this” is also a form of credibility.
Accountability with humanity
The best report is neither cold nor overly emotional. It shows people with dignity, resources with transparency, and decisions with method. That is what allows supporters to trust the project and the team to keep improving.
Next step
Find out how the project intends to measure impact and follow simple reports: what happened, what was used, what we learned and what will be tested next.
