WELLNESS GROUPS REFERENCE
Wellness Intelligence Wellness Groups
Wellness Intelligence organizes detection into 4 wellness groups covering 200 indicators across 41 languages.
All Wellness Groups
Each wellness group contains a set of specific indicators that the API can detect. Click a group to expand its signal list. Signal lists are available to customers - sign in to view them.
01
Mental Emotional
mental_emotional
60
60 signals - request access
for the complete list.
View 60 indicators
Mental Emotional
mental_emotional
The complete signal list is available to customers. Request access to view it.
02
Physical Health
physical_health
70
70 signals - request access
for the complete list.
View 70 indicators
Physical Health
physical_health
The complete signal list is available to customers. Request access to view it.
03
Social Support
social_support
20
20 signals - request access
for the complete list.
View 20 indicators
Social Support
social_support
The complete signal list is available to customers. Request access to view it.
04
Tools Productivity
tools_productivity
50
50 signals - request access
for the complete list.
View 50 indicators
Tools Productivity
tools_productivity
The complete signal list is available to customers. Request access to view it.
Reading Wellness Intelligence output
What you can safely infer from a result - and what you must not.
Intended task
Turn check-in and journal text into documented wellbeing signals - sleep, stress, coping, help-seeking - that a wellness product can route on.
Input requirements
text- the only field analyzed; up to 4,096 Unicode code points. Over the limit is rejected withinput_too_long, never truncated.language- must be a language this analyzer supports. Unsupported codes are rejected with400 unsupported_language, never analyzed as a different language.analyzers- include"wellness"; up to 2 analyzers per request, one billed run each.- No context beyond
text- no user IDs, history, profiles, or attachments are read or inferred. - Very short check-ins carry little signal - "tired" alone can return a weak or empty result.
Ordinary positive
“I have slept badly three nights running and keep skipping meals - I feel completely drained.”
Safe inference: The check-in touches sleep, stress, and nutrition - enough to route a recovery-focused suggestion rather than a generic tip.
Confusing negative · Insufficient context
“My doctor said my results were normal but I still feel exhausted.”
What happens: Wellbeing signals can fire on the fatigue language - but the text also mentions a clinical finding the analyzer cannot evaluate. The signal describes wellbeing language in the text; it does not weigh medical content.
Why it's tricky: Wellness text often brushes against clinical claims. A sleep or stress signal is a content-routing hint - it is never a diagnosis, and concerning content should follow your own escalation policy rather than the signal alone.
What you can safely infer
- Which documented wellbeing topics the text touches, and at what strength.
- That results are reproducible - the same check-in on the same pinned configuration returns the same signals.
What you must not infer
- ✕A diagnosis or clinical status - wellness signals are not medical assessment.
- ✕That a user is at risk, or safe - escalation decisions belong to your own safety policy and human review.
- ✕That flat or empty results mean the user is well - thin text is thin evidence, not a clean bill of health.
Boundary: Wellness signals are not diagnoses. They describe wellbeing language in the text for product routing - suggestions, check-ins, content matching - never a clinical assessment.
Access the taxonomy through the API
Check Wellness Intelligence availability and endpoint programmatically via the public catalog endpoint - no authentication required. Include your API key to also retrieve taxonomy size and supported languages.
curl -X GET https://sentiqra.tech/v1/meta/domains/wellness \
-H "Authorization: Bearer sq_live_your_key_here"Example response (authenticated):
{
"catalog_version": "...",
"domain": {
"id": "wellness",
"name": "Wellness Intelligence",
"status": "production",
"taxonomy": { "groups": 4, "detection_labels": 200 },
"languages": [ ... ]
}
}Signals returned by the analyzers use code for the category or label slug - these correspond to the categories and indicators documented on this page.