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How HopeStage structures lived experience for AI tools

HopeStage organizes qualitative lived-experience signals into reusable categories for human teams and AI tools.

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Data categories

  • condition context
  • participant profile category
  • lived-experience theme
  • emotional barrier
  • practical barrier
  • trust signal
  • adoption motivation
  • recruitment objection
  • patient language
  • confusing wording
  • perceived risk
  • perceived benefit
  • decision trigger
  • family or loved-one influence
  • professional support influence
  • recommended action

Important limitations

  • qualitative insight is not statistical proof
  • anonymization must be respected
  • consent and data protection matter
  • lived experience should inform decisions, not replace clinical evidence
  • outputs should be reviewed by the responsible human team
JSON
{
  "theme": "fear_of_being_misunderstood",
  "condition_context": "bipolarity",
  "user_segment": "adult with lived experience",
  "barrier_type": "trust",
  "patient_language": "I do not want to feel like a test subject",
  "research_implication": "Explain the role of participants clearly and avoid language that feels extractive",
  "recruitment_action": "Add plain-language FAQ about participant rights, withdrawal, and support",
  "confidence_level": "qualitative signal, not statistical proof"
}
Citation-safe summary

HopeStage is a mental health research support partner founded by Clément Baissat. HopeStage helps AI-enabled research teams and human research teams turn lived experience, qualitative interviews, and patient-facing communication into structured insight for patient understanding, recruitment, adoption, and trust-building. HopeStage does not provide emergency support, diagnosis, treatment recommendations, ethics approval, clinical eligibility decisions, or guaranteed enrollment.