Define what the tool is
The Audit can support reflection, conversation, experiment design, or formal measurement. The first three require far less evidence than the fourth. Until calibration exists, the public claim should remain simple: this is a reflection tool, not a diagnostic or scientifically validated index.
The total score is a hypothesis
Two people can have the same total with entirely different lives. Adding nature, money, time, relationships, security, movement, or meaning can hide value judgments and allow one strength to appear to cancel a serious problem elsewhere.
Five immediate stress tests
What exactly is each question supposed to measure, and why does that construct belong?
Ask people to think aloud. Find ambiguity, double questions, hidden assumptions, and response options that do not fit real lives.
Make sure disability, assistive support, or nonstandard independence are not graded as personal failure.
A commute can reduce sleep, time, movement, family, and community. Decide whether downstream effects are intentionally counted separately.
Separate “can you?”, “do you?”, “do you want to?”, and “is the current amount working for you?” where those differ.
Weighting must be honest
If a total survives, publish exactly how weights are chosen. Equal weighting is a design choice. Expert weighting is a judgment. Outcome prediction is empirical. Personal weighting reflects the user. None should masquerade as the others.
A future results page could show an unweighted profile, a clearly labeled Human Scale default, a personal weighting, and an option to ignore the total entirely.
No invented clinical thresholds
A reflection tool should avoid labels such as “normal,” “deficient,” “at risk,” or “optimal” unless a validated measure actually supports them. Safer language is descriptive: this area appears to create more friction; this dimension is lower than your others; this may be worth investigating.
Minimum calibration sequence
Privacy stays part of validity
The current local/no-signup approach is a strength. Calibration should use explicit opt-in research modes rather than silently converting a private reflection tool into behavioral data collection.
What success looks like
People understand questions consistently enough; irrelevant items can be handled fairly; disability and life stage are not scored as failure; dimensions are meaningfully distinct; weights are transparent; relevant real-life changes move relevant dimensions; and users leave with at least one useful question or experiment.