The science
Programme Type is a structured model of how childhood shapes adult behaviour. Here is what it measures, what it stands on, and what it is not.
Every type is two things at once: the wound it grew from, and the mask you built to cope with it. The wound is what your early world was short on. The mask is the strategy you reached for to stay safe, loved, free, or alive. Where the two meet is your type.
There are four wounds, or roots: Safety, Connection, Self and Aliveness. And six masks: the Achiever, the Caretaker, the Good One, the Self-Reliant, the Performer and the Withdrawer. Six masks across four roots gives twenty-four types, plus a lighter pattern, the Settled, for those whose early ground was steady enough that no single wound took hold.
You rate a series of statements on a scale from nought to ten, by how true each one is of you. The statements are written to map onto the roots and the masks, and the shape of your answers locates you on the grid. There are no trick questions and no right answers, only how closely each statement fits your life.
The framework stands on established ground. It draws on attachment theory and how early bonds shape the way we relate; on schema therapy and the early patterns it describes; on internal family systems and the idea of protective parts; and on what is known about how the nervous system carries early experience in the body. Programme Type brings these together into one structure. The framework, the twenty-four types and their names are our own.
This is a tool for self-understanding, not a clinical instrument. It does not diagnose, it is not therapy, and it is not a replacement for professional help. It is a mirror, made specific enough to be useful and honest enough to be trusted. If you are struggling, please speak to a qualified professional or your GP.
We hold this work to a real standard. The model is built on established theory and refined against thousands of responses, and we treat validation as ongoing: testing the instrument's reliability and consistency as the data grows, and improving it as we learn. We would rather be transparent about that than overclaim.