'Where is this coming from?' Uncovering Trustworthiness Ideals in AI-powered Peripartum Information Seeking

CLAIM (Claim-Specific Citation Network audit, sometimes called CSN) is a forensic method for testing whether a scientific or medical claim's authority is supported by evidence or by citation dynamics. It detects citation bias, amplification, citation diversion, citation transmutation, dead-end citation, and back-door invention.

The ASSAY skill runs a structured, CLAIM-compatible extraction and integrity assessment on an article. Output is a verdict (sound, mixed, flagged, problematic, or cascade), a count of claims extracted, the central key claim, and an integrity note describing the structural read.

This scan restricts ASSAY to peer-reviewed publications and preprint servers. Journalism, opinion pieces, and government documents are evaluated under different frameworks (CAIHL for power and agency; editor's note for context).

ASSAY found the central claims well-supported by the underlying evidence; methodology stands; the integrity-of-citation check raised no structural concerns.

Patients using AI tools during the peripartum period build trust through a distinct set of ideals — source provenance, alignment with their own clinical context, and explicit acknowledgment of uncertainty — that current AI-tool design treats as add-ons rather than constitutive features.

Total claims extracted from the article: 7. The key claim is the single most load-bearing assertion the rest of the argument depends on.

Qualitative method (interviews + thematic analysis) is well-matched to the trust-construction research question. Sample size and recruitment frame are appropriate for theory-building but not for prevalence claims; the paper does not over-claim. The trust-grammar finding is the right level of empirical contribution — generative for design, not generalizable to a population estimate. Preprint, not peer-reviewed.

Editor's note: Peripartum is the highest-stakes patient-AI segment because the decision window is short and the cost of error is two lives. The trust ideals the paper documents are the trust ideals every patient-AI tool will eventually have to ship against — peripartum is just where it gets surfaced first.

Summary: arXiv: Qualitative study with peripartum patients (pregnancy through postpartum) on what counts as 'trustworthy' when they use AI tools for health information — surfaces a distinct trust grammar the AI-tool design literature has not built around.

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