MINORIA APPLICATION

This application ensures SLMs provide consistent, evidence-aligned support while respecting the complexity of PTSD care—bridging gaps in accessibility without compromising clinical rigor.

Use Cases

Screening Tool: The app collects patient histories via culturally adapted questionnaires 5.
Treatment Support: SLMs suggest personalized coping strategies aligned with CBT protocols 4.
Resource Allocation: Predictive analytics identify phenotypes needing targeted outreach 7.

Scenario Design Principles

Diverse Trauma Personas

Scenarios cover PTSD subtypes (e.g., combat, assault, disaster trauma) and demographic variations (age, cultural background) to improve generalization2.
Example: TIDE dataset’s 500 synthetic personas with tailored linguistic markers (e.g., avoidance semantics in abuse survivors)2.

Three-Factor Empathy Framework

Scenarios train models to:

Recognize emotion: Identify distress cues like hypervigilance (“I can’t stop checking the locks”)2.
Normalize distress: Validate experiences without pathologizing (“Your reaction makes sense given what happened”)2.
Reflect supportively: Suggest evidence-based strategies (grounding techniques from CBT)1.

Clinical Validation Workflow

Psychologist Review

All synthetic dialogues are vetted for:
- Emotional plausibility: Aligns with DSM-5 PTSD criteria1.
- Trauma sensitivity: Avoids retraumatizing language (e.g., explicit details of abuse)2.
- Cultural relevance: Adapts responses to minority communication styles2.

Bias Mitigation

Scenarios include adversarial examples to reduce harmful generalizations (e.g., avoiding assumptions about gender-based trauma)2.

Training Objectives

Contextual Response Generation

Models learn to adjust replies based on:
- Trauma phase: Acute crisis vs. long-term management1.
- Support type: Emotional validation vs. skill-building1.

Ethical Guardrails

Scenarios enforce boundaries:
- Prohibit treatment recommendations beyond first-aid support.
- Redirect high-risk disclosures to human professionals2.

Evaluation & Iteration

Human Feedback Loops

Clinicians rate responses on:
- Safety: 94% reduction in harmful outputs post-fine-tuning2.
- Cultural competence: 37% improvement for minority dialect handling2.

Adaptive Learning

Models update via federated learning using anonymized clinician corrections (e.g., refining hyperarousal responses)2.