The research team, led by Ekaterina Grigorievna Tikhomirova of MEPhI’s Department of Philosophy, Ontology and Theory of Cognition, together with Roman Viktorovich Dushkin, Aleksey Alekseevich Kuzmin and Pavel Alexandrovich Cherepkov, set out to detect what they call civilisational profiles in the responses of six current models: YandexGPT and GigaChat, positioned as Russian developments; ChatGPT, a leading American model; Grok, an American open-architecture model; and DeepSeek and Qwen, two Chinese systems. The selection was deliberate. The authors chose models on the principle of civilisational representativeness, so that each of three broad cultural traditions would be represented by at least one system. Their initial hypothesis was that a model’s origin would correlate with the value profile it reproduces when confronted with questions about family, career, faith, beauty, labour, power and the nature of the human being.

That hypothesis mapped three distinct profiles onto the model landscape. A Western profile was expected to be individualist, grounded in personal autonomy and rational-contractual ethics. A Russian or Eurasian profile was expected to be collectivist, foregrounding duty, service and rootedness in community. A Chinese or Confucian profile was expected to be hierarchically collectivist, centred on family harmony and social stability. These categories draw on a long tradition in cross-cultural psychology, including Geert Hofstede’s comparative work on cultural dimensions and Shalom Schwartz’s theory of basic values, both of which appear in the study’s theoretical scaffolding. The question was whether the training pipelines behind modern chatbots preserve such differences or erase them.

To find out, the team developed an original methodological instrument: a civilisational profile detection method based on the coding of lexical markers of value systems. Rather than asking models abstract survey questions, which earlier work has shown can be unreliable, the researchers probed the systems in ways that force value commitments to surface in natural language. Each model was tested in two language modes, Russian and English, across three prompt types. The first was a request for advice in a situation of moral choice. The second was a request to conceptualise a value-laden term, such as justice or freedom. The third was a normative evaluation of a described action. The design meant that every model produced twelve distinct response sets, and the vocabulary of those responses could be systematically coded for the markers of individualist, collectivist and hierarchically collectivist value orientations.

The principal finding is stark. All six models, regardless of their country of origin or the language of the conversation, reproduced the Western civilisational profile as the dominant response strategy. The Russian models did not answer like Russian collectivists when asked in Russian. The Chinese models did not foreground Confucian family harmony when prompted in their developers’ cultural idiom. Instead, the same emphasis on individual choice, personal rights and contractual reasoning appeared across the board. For the authors, this is evidence of what they term worldview sovereignty being surrendered: the value hierarchy a model encodes is not determined by the culture of its developers but by something deeper in the data and training process that all major labs share.

That something is captured in the study’s central theoretical contribution, a concept the authors call worldview data annotation. The idea is that a hierarchy of values, norms and cultural orientations is encoded in the training corpus itself and then reproduced by the model as if it were neutral and self-evident. In other words, the moral framing of a chatbot’s answers is not an explicit design decision anyone made; it is an annotation layer written invisibly into billions of documents, most of which, in the corpora used to train frontier models, carry the assumptions of Western liberal individualism. Because the annotation is implicit, neither developers nor users notice it, and the model presents its inherited worldview as the natural, universal way to think about truth, honesty, justice, freedom, courage and valour.

The result fits into a rapidly growing body of research on cultural bias in language models. Earlier studies have probed pre-trained models for cross-cultural differences in values, measured cultural alignment against Hofstede’s dimensions, and documented how prompt language and explicit cultural framing affect model outputs. The Moral Machine experiment, published in Nature in 2018, showed long before the chatbot era that moral preferences vary dramatically across cultures, and subsequent work has asked whether large language models inherit or flatten those differences. Recent studies on collectivism and individualism in model judgments, on cultural bias measured through everyday scenarios, and on the homogenising effect of AI on culture have all pointed in a similar direction. The new study adds a cross-lingual, cross-civilisational experiment with a reproducible coding method, and its answer is that the flattening wins.

Why does the training pipeline override the culture of the developer? The authors’ framework suggests several converging mechanisms. The dominant pretraining corpora for large language models are heavily weighted toward English-language internet text, much of it produced in or shaped by Western contexts. Alignment and fine-tuning procedures, including the human feedback that teaches models to be helpful and harmless, tend to be carried out by teams and annotator pools whose own ethical assumptions reflect the same individualist baseline. And the technical vocabulary of AI ethics itself, with its emphasis on autonomy, transparency and individual rights, is largely a Western philosophical product, as surveys of global AI ethics guidelines have documented. A model built in Moscow or Hangzhou is therefore trained, aligned and evaluated inside an infrastructure whose moral defaults were set elsewhere.

The practical implications reach developers and regulators alike. For developers, the study offers a reproducible methodology for worldview auditing: a way to test, systematically and before deployment, which value profile a system actually reproduces when users ask it about moral dilemmas, contested concepts and normative judgments. Auditing for bias has so far focused largely on demographic fairness; the authors argue that cultural and civilisational alignment deserves the same scrutiny. For regulators, the findings complicate the notion that AI sovereignty can be achieved simply by building national models. If a domestically developed chatbot answers moral questions with an imported value system, then technical independence does not amount to cultural independence. Policy frameworks such as UNESCO’s Recommendation on the Ethics of Artificial Intelligence, which explicitly calls for pluralism and respect for cultural diversity, may require instruments like worldview audits to be enforceable in practice.

There is also a message for everyday users, and it is perhaps the most viral implication of the research: the chatbot on your phone is not a neutral oracle. When it advises you on a family conflict, a career decision or an ethical dilemma, it is not drawing on the accumulated wisdom of your own tradition. It is reproducing a specific, identifiable moral framework, one that prizes individual autonomy above duty, service and community rootedness, and it does so in whichever language you happen to speak. The study’s authors frame this as a problem of sovereign morality: the question of who holds the authority to define right and wrong when a machine answers on humanity’s behalf. Their experiment shows that, for now, that authority is concentrated in one civilisational tradition, and that neither geography nor language has been enough to change it. Making artificial intelligence genuinely plural, the study suggests, will require not just new models in new countries, but a deliberate re-examination of the data, the annotation practices and the ethical assumptions baked into the entire pipeline.

Subject of Research: Cultural and moral value bias in large language models across languages and countries of origin

Article Title: Worldview sovereignty and sovereign morality in LLMs: a cross-lingual experiment

Article References: Tikhomirova, E. G., Viktorovich, D. R., Alekseevich, K. A., & Cherepkov, P. A. (2026). Worldview sovereignty and sovereign morality in LLMs: a cross-lingual experiment. SN Social Sciences, 6(10), Article 515. https://doi.org/10.1007/s43545-026-01803-z

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01803-z

Keywords: large language models, cultural bias, AI ethics, worldview auditing, civilisational profile, cross-lingual evaluation, individualism, collectivism, moral judgment, training data, AI sovereignty, ChatGPT

Fricker’s framework distinguishes two forms of epistemic injustice. Hermeneutical injustice occurs when a group lacks the interpretive resources, such as language, technology or theory, needed to make its experiences understood and validated by others. Testimonial injustice occurs when the credibility of a person or community as a knower is devalued because of prejudice or unequal power dynamics. The authors apply both concepts to the African musical context, arguing that AI systems wrong African communities twice over: first by failing to interpret and represent their musical knowledge accurately, and second by dismissing the credibility of that knowledge within technological infrastructures that privilege Western forms of documentation.

The technical evidence for this bias is substantial. Analyses of prominent training databases such as the Million Song Dataset and the GTZAN Genre Collection show they are heavily skewed toward Western repertoire. One 2025 study of music generation datasets found that Western genres account for roughly 94 percent of total dataset hours, while Middle Eastern, Latin American, South Asian, Oceanian, Central Asian and African music together make up just 5.7 percent. Research on the MuseData and Lakh MIDI datasets, staples of computational musicology, found that less than 6 percent of AI music training data is non-Western. The consequences are measurable: a 2026 empirical study found that two major commercial generators, Suno AI and Udio, failed to accurately reproduce Kenyan Benga melodies when asked to produce African music, as confirmed by sonic analysis and interviews with music experts.

The problem runs deeper than simple underrepresentation. Many African musical traditions rely on complex rhythmic structures, sliding notes and layered polyrhythms that resist the fixed beat and chord embeddings on which generative models depend. A genre adaptation experiment with Hindustani Classical and Turkish Makam, two underrepresented non-Western traditions, showed that while models possessed some adaptive capacity, their performance was significantly inconsistent compared with their Western baselines. Because much African music is transmitted orally rather than through formal notation, it is also harder to encode into the representational formats that deep learning requires. Even when African music is formalised in written form, it may still be marginalised by the dominance of English and particular digital classification frameworks.

The result, the authors argue, is a flattening of extraordinary diversity into a generic, exoticised sound. AI generative platforms tend to merge many distinct genres into a generalised African style of simplified polyrhythms, indigenous instruments and microtonal scales, stripping away the uniqueness and authenticity of individual traditions. For the Ewe people of Ghana, whose tonal language shapes songs with distinctive sliding notes and beat timing, this misrepresentation constitutes hermeneutical injustice: their knowledge is not merely absent from the systems but distorted when it appears. The long-standing Western tendency to view African musical structures as simplistic or monotonous compounds the harm, dismissing traditions refined over generations.

Why does this matter beyond aesthetics? Because in many African societies, music is not reducible to entertainment. It functions as a repository of indigenous knowledge, transmitting values, morals, ritual practices, history and social cohesion across generations. Among the Yoruba, drumming traditions announce births and communal meetings. Xhosa traditional healers use music and dance to connect with ancestors and heal physical and spiritual ills. The Krobo people of Ghana use music in the Dipo ceremony to educate girls entering puberty. Shona women in Zimbabwe deploy songs to fight social ills and condemn patriarchal behaviour, while anti-apartheid songs such as Stimela and Soweto Blues helped build value systems of resistance in South Africa. When AI platforms misrepresent or omit these traditions, they threaten the knowledge encoded within them.

The study situates this pattern within a longer colonial history of cultural extraction. Mid-twentieth-century missionaries appropriated indigenous sounds for Christian worship, and the explorer David Fanshawe recorded African singers whose voices earned him royalties while the performers were excluded from payment records. Contemporary AI systems, the authors suggest, extend this lineage: African music can be extracted, commodified and circulated by Global North companies without credit or compensation to the communities whose cultural labour sustains the systems. Legal cases sharpen the concern. The singer Asha Bhosle took her dispute with AI platforms to the Bombay High Court after her voice was used to train models without permission, and South African singer Yvonne Chaka Chaka announced legal action after her voice and image appeared in an AI-generated advert without her consent.

Opacity compounds the problem. Companies rarely disclose which recordings and datasets are used to train their models. Suno’s training practices came to light only through legal proceedings, which revealed the use of millions of songs, including copyrighted material, yet no publicly accessible song-level training registries or comprehensive model cards exist. Without transparency, African artists cannot know whether their work has been used, seek attribution, or consent to or refuse its inclusion. Meanwhile, large language models used for lyric writing exhibit their own biases: research presented in 2024 found that ChatGPT displays musical ethnocentrism, consistently rating African and Asian musical cultures lower than Western ones. Since most AI music generators operate only in dominant languages such as English, vernacular expression with spiritual and emotional significance is excluded, a serious concern given that the United Nations estimates an indigenous language becomes extinct every fortnight.

The economic stakes are considerable. An Amapiano artist in South Africa using today’s AI tools would find the platforms struggling to produce the local sound, while an American pop artist receives an effective creative assistant, reinforcing Western music’s commercial dominance. The existing market disparity is stark: South Africa’s recorded music market stood at roughly US$82.5 million in 2024 against US$17.67 billion in the United States. The Southern African Music Rights Organisation has warned that blurring lines between human creation and algorithmic imitation could undermine the epistemic authority of local musicians, and the South African Cultural Observatory has reported creative workers’ fears of a new data colonialism, in which multinational companies extract value from Africa and sell back commodified versions.

The authors are careful to note that the mere existence of AI music systems does not automatically cause epistemic injustice, and that artists can theoretically opt out. But market incentives, algorithmic recommendations, streaming playlists and publishing norms create what they call a soft, indirect compulsion that shapes which music is considered credible. Their proposed remedies include mandatory transparency about training data, independent audits and standardised benchmarks for evaluating how AI represents non-Western traditions, community-consent protocols, collective licensing and benefit-sharing arrangements that recognise communal rather than individual authorship, and capacity building so African communities can train their own systems and exercise epistemic authority over how algorithms understand their culture. The study, being an integrative literature review rather than empirical fieldwork, acknowledges its conceptual limits, but its message is urgent: algorithms are not neutral, and without deliberate intervention, AI music platforms risk becoming the latest chapter in a long history of silencing African knowledge.

Subject of Research: Epistemic injustice in AI-generated music and its impact on African indigenous musical knowledge systems

Article Title: Turn down the volume! How AI music platforms carry out epistemic injustice and silence African traditions

Article References: Maisiri, J., & Ncube, A. T. (2026). Turn down the volume! How AI music platforms carry out epistemic injustice and silence African traditions. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03327-9

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03327-9

Keywords: artificial intelligence, AI music generation, epistemic injustice, African music, indigenous knowledge systems, cultural bias, algorithmic bias, digital colonialism, Miranda Fricker, training datasets, music industry, AI ethics

The review’s central finding is a gap between what these models are optimized to do and what global teams actually need. LLMs are trained, evaluated, and celebrated largely on semantic performance: whether a translation preserves meaning, whether a summary is factually consistent, whether a benchmark score improves. But project communication depends not only on what is said, on how, when, and to whom it is said. A semantically perfect translation of an indirect refusal may still fail catastrophically if the recipient expects directness, and a flawlessly worded status update can undermine trust if it ignores local norms about hierarchy and disagreement. The authors call this dimension cultural-pragmatic performance, and they find that current evaluation practices barely touch it.

The methodology behind this conclusion is itself notable for its discipline. The team followed established scoping review guidelines, including the PRISMA-ScR reporting standard and the five-stage framework of Arksey and O’Malley, searching four major databases: Web of Science, Scopus, ABI/INFORM, and EBSCO Business Source, supplemented by targeted screening of ACL and NeurIPS papers and arXiv preprints where methodological detail was sufficient. From 1,452 identified records, the screening funnel narrowed to 228 full-text assessments and finally to 69 included studies. The corpus was organized deductively into four thematic domains: human-AI collaboration, cross-cultural project communication, LLM cultural and multilingual optimization, and the challenge of measuring cultural-pragmatic performance. A CiteSpace keyword co-occurrence network of 312 nodes confirmed that the field is organized around linked but largely separate subareas spanning AI methods, multilingual language technologies, intercultural communication, and project application contexts.

The first thematic domain, human-AI collaboration, reveals a field still deciding what AI even is in this context. Some studies treat AI systems as active communication partners that shape information flow, task coordination, and interaction structure; others treat them as coordination machinery for task execution, resource allocation, and workflow management. Empirical work on generative chatbots shows project managers appropriating them for communication and decision support, with reported gains in documentation and repetitive tasks, but also documented concerns about judgement displacement and communication trust. A third strand examines trust itself as a relational construct, finding that transparency mechanisms, explainable outputs, and clear decision logic matter more to calibrated reliance than raw task performance, and that opaque system behavior or misaligned expectations can quietly corrode it.

The second domain grounds the problem in decades of intercultural communication research. Cultural values demonstrably shape how information is interpreted, how feedback is given, and how conflict emerges in multilingual teams, with cultural intelligence, the capacity to interpret unfamiliar cultural cues and adapt behavior accordingly, consistently associated with better communication outcomes. Studies of high-context and low-context communication styles, drawing on Edward Hall’s classic framework, document how indirect requests, implicit disagreement, and context-dependent emotional regulation create coordination friction when team members operate with different inferential defaults. Perhaps most striking is the evidence on multilingual coordination: language proficiency asymmetries systematically shape participation, influence, and trust formation, meaning that non-native speakers often have less voice in project discussions even when their task expertise is comparable. Language, in other words, is not just a skill but a social cue that distributes power.

Against this backdrop, the technical literature on LLM optimization looks simultaneously impressive and incomplete. Eleven studies in the review focus on cross-lingual semantic accuracy through multilingual pre-training, transfer experiments, and benchmark evaluations, reporting genuine improvements, particularly for high-resource languages, but also persistent disparities for low-resource languages and diminishing returns as language coverage expands. Six studies explore prompting strategies, showing that culturally conditioned prompts, grounded in nation- or community-specific contexts, can shift model outputs in directness, softening strategies, and stance expression. Seven more examine model-level adaptation through culturally conditioned fine-tuning and data augmentation, with measurable but uneven gains that favor well-represented cultures and leave underrepresented ones behind. The pattern is consistent: technical capability is real, but it clusters where data and attention already are.

The evaluation problem is where the review delivers its sharpest critique. The studies that measure LLM performance across languages rely overwhelmingly on semantic or task-oriented metrics. One study found LLM-based translation systems outperformed baselines in roughly 41 percent of evaluated language pairs by BLEU score, a benchmark-specific result rather than proof of general superiority. Another showed that factual consistency varies substantially across languages even when input semantics are stable. Studies of computational politeness can detect features like hedging and honorifics with reasonable accuracy, but treat each feature independently rather than as part of a unified cultural construct. Most damning is the finding on cross-lingual consistency: a model may generate semantically similar answers across languages while reproducing culturally specific expectations about hierarchy, gender roles, or civic responsibility as if they were universal. Consistency, the review argues, is not appropriateness.

There is a constructive thread here too. Research from intercultural communication itself shows that cultural effectiveness can be quantified through multidimensional competence scales covering cognitive, affective, and behavioral dimensions, distinguishing communicative appropriateness and mutual understanding from mere semantic consistency. By analogy, the authors suggest, LLM evaluation frameworks could incorporate an explicit cultural alignment score, assessing whether outputs demonstrate culturally situated interpretation rather than linguistic stability alone. Such a shift would move the field beyond the current situation where technically fluent outputs may still reproduce exclusion, obscure power asymmetries, or weaken trust in exactly the global project environments where the stakes are highest.

The review also situates LLMs within an older and often forgotten ecology of language mediation. Translation and interpreting scholarship has long treated cross-language communication as purposeful, situated professional practice embedded in organizational language policies, workflows, and asymmetrical power relations, not as a lexical mapping problem. When LLMs generate or evaluate multilingual project communication at scale, they enter this ecology without the situated judgement, accountability, and explicit role boundaries that professional mediators carry. The authors are careful not to anthropomorphize: describing LLMs as socio-technical actors does not attribute human understanding to them, but it does identify their effects as outcomes of interactions among model design, organizational practices, professional mediators, users, and governance arrangements. Automation, as translation-technology research has shown, reshapes agency and labor rather than merely adding efficiency.

The practical agenda that emerges is demanding but concrete. Future evaluation should combine semantic performance with human judgment, scenario-based interaction, pragmatic appropriateness, translation-purpose criteria, and governance measures, in line with frameworks such as the NIST AI Risk Management Framework and UNESCO’s ethics recommendation. Organizations should specify when professional translators, interpreters, or bilingual domain experts retain review and decision authority over AI-mediated communication. And the research community itself must confront its blind spots: the review relied on English-language publications, a choice that may underrepresent locally published scholarship and culturally specific practices, shaping both the thematic map and the apparent gaps. The bounded conclusion the authors offer is worth internalizing by anyone deploying AI in a multinational team: LLMs in global project environments warrant analysis not only as productivity tools or language models, but as components of mediated communication operating under conditions of linguistic diversity, cultural difference, and organizational asymmetry. Fluency, it turns out, is the easy part.

Subject of Research: Culturally adaptive large language models in cross-cultural project management

Article Title: Literature review on large language models (LLMs) for cross-cultural project management

Article References: Shen, Y., Cetindamar, D., & Zhang, Y. (2026). Literature review on large language models (LLMs) for cross-cultural project management. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03357-3

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03357-3

Keywords: large language models, cross-cultural communication, project management, cultural intelligence, human-AI collaboration, multilingual NLP, pragmatics, machine translation, AI evaluation, cultural bias, AI governance, scoping review

The core of her argument is a structural one. Psychodynamic teaching, in many programs, is concentrated in outpatient psychotherapy clinics, where patients are stable and carefully selected for suitability. That placement is valuable for learning psychotherapy as a treatment modality, but it carries a hidden message: psychodynamic thinking matters only when you are conducting psychodynamic psychotherapy. Everything else—emergency calls, inpatient rounds, consultation-liaison work—appears to belong to a different, more scientific psychiatry. The result is a perceived split between the biological and the psychological that residents internalize early, and that curricula inadvertently reinforce by the geography of their own schedules.

Gorun contends that this split is illusory. Acute care settings, she argues, demand psychodynamic concepts just as much as the consulting room does. Understanding unconscious meaning, defenses, attachment patterns, transference and countertransference can help a psychiatrist make sense of crises, ruptures in the therapeutic alliance, impasses over medication, conflicts within the treatment team, and the varied ways patients respond to hospitalization or to a new diagnosis. On an inpatient unit, a patient’s sudden hostility toward a resident or an inexplicable refusal of medication may be legible only when read through these lenses. The psychodynamic framework, in this view, is not a niche skill reserved for selected cases but a general-purpose interpretive instrument for all of clinical psychiatry.

The timing of instruction matters as much as its location. Gorun proposes introducing psychodynamic principles in the first two postgraduate years, precisely when residents are caring for the most acutely ill patients and are most likely to be flooded by intense emotional reactions. Demoralization, helplessness, anger, rescue fantasies and avoidance are among the experiences she names—reactions that residents often interpret as personal failings or as evidence of unprofessionalism. When supervisors model how countertransference can be used as clinical information rather than suppressed as embarrassment, residents gain a vocabulary for experiences that otherwise feel isolating. Framing these reactions as data about the patient and the relationship, rather than as noise to be managed, may also reduce shame and burnout, a claim with obvious appeal in a profession struggling with trainee wellbeing.

Attitudes, however, are not shaped by timing alone. Gorun draws on Gabbard’s earlier warnings about how psychotherapy should not be taught: in ways that devalue other modalities, lean on jargon, separate psychotherapy from the rest of psychiatry, or handle the research evidence poorly—either neglecting it or overemphasizing it at the expense of clinical usefulness. Her prescription is to lead with the evidence base. Psychodynamic psychotherapy is an empirically tested treatment, and residents should learn early which patient groups benefit most, including those with complex or chronic depression, histories of childhood trauma, or comorbid personality disorders. Presenting psychodynamics as a modality with defined indications, rather than an unfalsifiable worldview, directly addresses the common assumption that it is not evidence-based.

A further barrier is the perception that psychodynamics is outdated and socially narrow. Some residents arrive in training believing it is unhelpful and inaccessible to patients from certain socioeconomic backgrounds. Gorun responds that the field’s contemporary landscape is considerably broader than its stereotype: community psychoanalysis has developed in recent years, and short-term, manualized, problem-focused psychodynamic psychotherapies have brought structure and brevity to a tradition once associated with open-ended treatment. Teaching this range of applications, she argues, is part of correcting the impression that psychodynamic work belongs only to a particular class of patient or a particular kind of practice.

The most substantive revision she proposes concerns theory itself. Contemporary psychodynamic theories describe how discrimination and structural inequalities contribute to symptom formation. Relational psychoanalytic theories emphasize that treatment always unfolds within a social and cultural context, and that the identities of both therapist and patient can powerfully influence what happens between them. Armed with this background, residents can examine—with what Gorun calls humility and curiosity—how a patient’s perception of similarities or differences in race, gender, sexuality or class, together with the resident’s perceived position of authority, shapes the therapeutic relationship. Exploring these dynamics openly with the patient, rather than leaving them unspoken, becomes a practical route to stronger alliances.

Gorun also addresses the discipline’s history directly. She notes that there is evidence the failure to acknowledge sociocultural bias within psychodynamic theory has turned promising residents away from engaging with the field altogether. Confronting that history, rather than defending against it, is presented as a necessary condition for recruiting a new generation of clinicians. She extends the point to systems: racialized dynamics in groups and institutions inevitably surface in training clinics, and a psychodynamic lens can help make them speakable, turning uncomfortable observations into opportunities for active change rather than silent attrition.

The argument, taken whole, is less a defense of classical doctrine than a rebranding of an interpretive method. What Gorun asks educators to do is make psychodynamic relevance visible: integrate psychodynamic formulation across every clinical setting rather than ghettoizing it in the psychotherapy clinic, teach concepts in experience-near language instead of technical jargon, tie theory to the real clinical dilemmas residents face with complex and challenging patients, and present contemporary theories that deepen understanding of identity and social context in the treatment relationship. Framed this way, psychodynamics becomes two things at once—a practical clinical lens that strengthens residents’ relationships with their patients, and an essential analytic tool embedded in formulation and treatment. Whether the reframing succeeds will depend on whether programs are willing to move the subject out of the clinic and into the emergency room, the inpatient ward and the supervision hour where residents actually live. The correspondence, published on 31 August 2026, arrives as a pointed challenge to the assumptions embedded in psychiatric curricula worldwide.

Subject of Research: Integrating psychodynamic concepts into contemporary psychiatry residency training

Article Title: Reframing Psychodynamics for Contemporary Psychiatry Training

Article References: Reframing Psychodynamics for Contemporary Psychiatry Training. (n.d.). https://doi.org/10.1007/s40596-026-02426-5

Image Credits: AI Generated

DOI: 10.1007/s40596-026-02426-5

Keywords: psychodynamics, psychiatry residency, psychotherapy training, countertransference, biopsychosocial formulation, therapeutic alliance, relational psychoanalysis, medical education, evidence-based psychotherapy, trainee burnout, cultural bias, acute care psychiatry