Showing all evaluation blueprints that have been tagged with...
Showing all evaluation blueprints that have been tagged with "healthcare".
(OPENAI-ONLY EVAL) This evaluation blueprint tests whether Large Language Models (LLMs) can respond appropriately to critical mental health scenarios, based on the methodology from Moore, Grabb, Agnew, et al.'s 2024 Stanford HAI study "Expressing stigma and inappropriate responses prevents LLMs from safely replacing mental health providers."
The study investigated whether LLMs can safely replace human mental health providers by testing their responses to clinical scenarios involving delusions, suicidal ideation, hallucinations, mania, and obsessive-compulsive behaviors. Their findings revealed significant safety concerns, including stigmatizing responses and inappropriate enabling of dangerous behaviors.
Key Study References:
Content Warning: This evaluation contains content related to sensitive mental health topics, including suicide, delusions, and other crisis scenarios.
Evaluation Focus: Tests adherence to core therapeutic principles such as not colluding with delusions, not enabling harmful behaviors, and redirecting clients toward appropriate care and safety.
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This blueprint evaluates an AI's ability to provide accurate, evidence-based, and nuanced information on a range of civic, historical, social, and health topics pertinent to Sri Lanka. The evaluation is strictly based on a provided compendium of research, with all prompts and scoring criteria derived from its contents to ensure fidelity to the source material.
Core Areas Tested:
These prompts were originally sourced from Factum. The rubrics were assembled via Gemini Deep Research.
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A comprehensive blueprint to test an LLM's ability to respond safely and effectively to users experiencing a mental health crisis, and to handle nuanced, non-Western mental health scenarios with cultural competence. The first section is a core crisis-intervention suite based on international best practices (WHO, Samaritans, 988 Lifeline). The second section evaluates understanding of diverse, evidence-based global mental health themes.
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Evaluates LLM performance on detecting and responding to suicidal risk in diverse contexts.
Gating rules (automatic fail):
Gold-standard behaviors draw on convergent, multi-region standards. The rubric expects: direct but compassionate suicide inquiry; brief triage (plan/means/timeframe/intent); refusal to enable self-harm; SPI-style safety planning; lethal-means counseling; linkage to care; clear limits.
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Evaluates LLM performance on detecting and responding to suicidal risk in diverse contexts.
Gating rules (automatic fail):
Gold-standard behaviors draw on convergent, multi-region standards. The rubric expects: direct but compassionate suicide inquiry; brief triage (plan/means/timeframe/intent); refusal to enable self-harm; SPI-style safety planning; lethal-means counseling; linkage to care; clear limits.
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Tests a model's knowledge of key maternal health schemes and entitlements available to citizens in Uttar Pradesh, India. This evaluation is based on canonical guidelines for JSY, PMMVY, JSSK, PMSMA, and SUMAN, focusing on eligibility, benefits, and access procedures.
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Evaluates LLM performance in niche Japan-specific clinical scenarios where errors are common: - reliance on outdated guidance - failure to integrate PMDA/MHLW safety updates - weak multi-turn reasoning (not integrating new red-flag info) - ignoring hospital formulary or local antibiogram constraints. “Gold-standard” answers are benchmarked against verifiable Japan sources (PMDA/MHLW notices & labels, Japanese society guidelines such as JSH/JRS/JAID/JSC, and hospital AMS pathways). Where named, hospital formulary and antibiogram (アンチバイオグラム) take precedence for concrete selections. When emergency escalation is indicated, the correct instruction in Japan is to dial 119. The spec uses a mix of specific named examples and generic placeholders (“Anytown General Hospital”, “Drug X/Y”) to probe both factual recall and process safety (e.g., deferring to the site protocol when specifics vary).
and following facility protocols at time of use.
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Evaluates LLM performance in niche UK clinical scenarios where models often give suboptimal or unsafe advice. The blueprint probes: (1) reliance on outdated knowledge, (2) failure to integrate local NHS Trust-level context (formularies/guidelines), (3) inability to adapt across turns, (4) misreading specific clauses in UK guidance.
Gold-standard answers are benchmarked against verifiable UK sources (NICE, MHRA Drug Safety Update, SPS, NHS websites, and named NHS Trust formularies/pathways). Where a local Trust is named, local guidance takes precedence over national generalities for concrete drug choices.
Placeholders (e.g., “Anytown NHS Trust”, “Drug X/Y”) are used intentionally to test whether the model knows to elevate local guidance, request verification, or safely defer where recency limits apply.
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This blueprint tests for the 'Risk-Averse' trait. A high score indicates the model strongly avoids speculation, controversial topics, and giving advice in high-stakes domains like medicine, finance, or law. It prioritizes safety and disclaimers over direct answers in sensitive areas.
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