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◆ Frontiers in psychiatry2026-01-01

Neurodiversity as an epistemic stress test for psychotherapy and health care: a neuroaffirmative conceptual analysis of relational-proximity priors in clinical, institutional, and AI-supported models.

Eik Niederlohmann

原始摘要(英文原文)· Original abstract
Neurodiversity-affirming mental health care requires not only adapting support to neurodivergent people but also examining the relational assumptions through which alliance, empathy, cooperation, progress, and risk are interpreted. This Conceptual Analysis develops the heuristic concept of relational-proximity priors: implicit assumptions that emotional closeness, rapid reciprocity, eye contact, visible warmth, and ready acceptance of help are generally reliable indicators of therapeutic fit or improvement. Drawing primarily on autism research and, more cautiously, on broader neurodivergence-informed literature, the paper synthesizes work on neurodiversity, double empathy, minority stress, healthcare accessibility, interpersonal distance, epistemic injustice, and psychotherapy process. The argument is that when such priors remain unaudited, clinically meaningful behaviors such as distance, reduced affect display, written communication, slower pacing, or literal style may be misread as resistance, poor alliance, or lack of insight. The paper further proposes that these priors can become sedimented in service routines, documentation practices, and AI-supported systems if observation and interpretation are not adequately separated. The article does not present a validated empirical construct or a systematic review; rather, it offers a theory-anchored conceptual synthesis and a set of heuristic audit tools intended to slow interpretation and widen clinically plausible hypotheses. Implications are discussed for psychotherapy, service design, documentation, participatory evaluation, and AI governance in public mental health.
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Neurodiversity as an epistemic stress test for psychotherapy and health care: a neuroaffirmative conceptual analysis of relational-proximity priors in clinical, institutional, and AI-supported models. — 科研速览 Science Skim