The study, led by Eileen Crehan of the University of Massachusetts Chan Medical School together with Kelly Whaling, Hannah Jones, Jacqueline Shinall, and Rachel Loftin of the telehealth provider Prosper Health, set out to answer a deceptively simple question: if a blinded expert reviewer examined the same assessment materials that a treating clinician used, would the two reach the same conclusion about whether the patient met DSM-5-TR criteria for autism spectrum disorder? This kind of diagnostic agreement study does not by itself prove that a test is accurate, but it is a foundational step. If two professionals looking at the same information routinely disagree, no amount of accessibility or convenience can rescue the assessment from unreliability.

The sample consisted of 100 adults who primarily communicated through spoken language, drawn from routine clinical care at the telehealth organization. Critically, the researchers deliberately stratified recruitment so that at least half of the participants were Black, Indigenous, and people of color; the final sample achieved 51 percent BIPOC representation. That design choice is notable because autism research has historically over-relied on white, male, and childhood samples, and documented racial and ethnic disparities in autism identification—in children as well as adults—raise legitimate concerns about whether diagnostic instruments perform equitably across demographic groups. By building diversity into the sampling plan from the start, the team aimed to generate agreement estimates that were at least plausible to generalize beyond a narrow population.

The assessment protocol itself was unusual in both structure and philosophy. Evaluations generally included two 90-minute structured interviews spanning ten clinical domains mapped directly onto DSM-5-TR criteria, along with behavioral observation, standardized questionnaires, and collateral information from informants when available. The interview and the broader assessment pathway were developed with input from autistic adults, who gave feedback on interview content, question framing, sensory considerations, and the overall experience of being assessed. The team describes the approach as neurodiversity-affirming: rather than framing autism as a collection of deficits to be uncovered, clinicians were trained to affirm clients’ lived experiences while still applying explicit, criterion-level decision rules to determine whether diagnostic thresholds were met.

That combination—affirming stance plus rigid structure—is the study’s most provocative claim. Skeptics of neurodiversity-affirming practice have sometimes worried that reducing deficit-based language would soften diagnostic rigor, leading clinicians to rubber-stamp autism diagnoses. The authors push back directly on this idea, arguing that affirming clients’ experiences and setting aside deficit-based assumptions does not require presuming an autism diagnosis. The structured interview’s criterion-level decision rules functioned as guardrails: every clinical judgment had to be anchored to specific DSM-5-TR criteria, documented on structured rating forms, and traceable through the written record that the blinded reviewer would later examine.

So what did the numbers show? When the blinded expert—Crehan, who had no employment relationship with the telehealth organization and did not supervise any of its clinicians—reviewed the de-identified assessment materials, overall agreement with the original clinicians was 86.0 percent, with a 95 percent confidence interval running from 77.9 to 91.5 percent. Translated into Cohen’s kappa, the standard statistic for inter-rater agreement that corrects for chance agreement, the value was 0.684 with a confidence interval of 0.531 to 0.836. The authors had preregistered a kappa benchmark of 0.66 before accessing any data, and the point estimate cleared that bar—although, as the researchers candidly note, the confidence interval extends below it, meaning the data cannot rule out more modest agreement.

The fine-grained breakdown adds texture to the headline figure. Original clinicians classified 68 percent of the sample as autistic, while the blinded expert reviewer classified 66 percent—suggesting neither party showed a systematic inflation bias. Positive percent agreement, meaning the proportion of clinician-assigned autism diagnoses the expert confirmed, was 90.9 percent. Negative percent agreement, the proportion of non-autism classifications the expert confirmed, was 76.5 percent. In other words, when a clinician concluded that a patient was autistic, the independent reviewer very often agreed; disagreement clustered disproportionately in the smaller group of cases where clinicians concluded that criteria were not met. In a diagnostic context where a missed or delayed identification carries real consequences—lost access to accommodations, mental health supports, and self-understanding—that asymmetry is arguably the conservative kind of error to have.

Exploratory subgroup analyses hinted at lower agreement among BIPOC participants, although the authors are careful to stress that these estimates were imprecise, reflecting the limits of subgroup sample sizes. The finding echoes a long literature documenting racial and ethnic disparities in autism identification, including earlier diagnosis gaps and differential clinical profiles at referral, and it flags measurement equity as an open question rather than a settled one. The full R analysis code is publicly available on the Open Science Framework, an unusual degree of transparency for clinical research that allows independent analysts to scrutinize exactly how every number was produced.

The researchers are equally explicit about what the study does not show. Because the blinded reviewer relied on substantially the same information as the treating clinicians—interview transcripts, rating forms, questionnaires, and collateral reports—the design tests agreement under shared-information conditions, not diagnostic accuracy in the strict sense. A reviewer working from the same records as the original assessor could, in principle, inherit the same errors embedded in those records. Establishing criterion validity would require independent prospective validation: separate evaluations conducted without information overlap, multiple blinded reviewers, and ideally comparison against a reference standard. The authors call for exactly that next phase, and they also note the tension inherent in their own positionality: several authors are employees of the telehealth organization whose protocol was evaluated, and most of the authorship team identifies as neurodivergent. The study’s mitigation strategies—preregistration, random case selection, de-identification, a blinded non-employee reviewer, and transparent reporting of all deviations from the preregistered plan—were designed to counter both commercial and proximity-to-subject biases, but the dual roles are disclosed for readers to weigh.

For adults seeking assessment, the practical implications are nonetheless striking. Telehealth autism diagnosis accelerated during the COVID-19 pandemic and has since accumulated a growing evidence base in children, but adult-specific validation has lagged. Adults present particular challenges: decades of accumulated coping strategies, camouflaging behaviors that can mask core traits in structured observation settings, and co-occurring psychiatric conditions that complicate differential diagnosis. A structured, criterion-mapped interview delivered over video, developed with autistic adults and now shown to yield substantial agreement between independent raters, could meaningfully widen access to diagnosis for people for whom in-person specialist assessment is logistically or financially impossible. At the same time, the equity signals in the subgroup data and the shared-information limitation are reminders that accessibility and validity are separate questions, and that the field’s next task is to test these protocols against fully independent evaluations. If that work confirms the present findings, the default pathway to an adult autism diagnosis—long defined by scarcity, geography, and outdated instruments—may be on the verge of a genuine structural shift.

Subject of Research: Diagnostic agreement of a structured telehealth-based adult autism assessment mapped to DSM-5-TR criteria

Article Title: Diagnostic Agreement of a Structured Telehealth Adult Autism Assessment

Article References: Crehan, E., Whaling, K., Jones, H., Shinall, J., & Loftin, R. (2026). Diagnostic Agreement of a Structured Telehealth Adult Autism Assessment. Journal of Autism and Developmental Disorders. https://doi.org/10.1007/s10803-026-07571-2

Image Credits: AI Generated

DOI: 10.1007/s10803-026-07571-2

Keywords: autism, telehealth, adult diagnosis, DSM-5-TR, diagnostic agreement, Cohen's kappa, neurodiversity-affirming care, BIPOC health equity, psychological assessment, preregistration, Diagnostic, Agreement

The authors situate their argument against a backdrop of dramatic epidemiological change. Population surveys published between 2008 and 2019 estimate that 0.3 to 0.5 percent of adults and 1.2 to 2.7 percent of children and adolescents identify as transgender, with consistent upward temporal trends across countries. Referrals to specialist pediatric gender services tell an even starker story: a systematic review covering 11 countries found referral rates below roughly 2 per 100,000 young people in 2010, rising to between 6 and 18 per 100,000 by the mid-2010s. At the Amsterdam clinic, adolescent referrals climbed from fewer than 20 in 2000 to around 230 in 2016. The composition of the referred population has also shifted, with the growth driven primarily by adolescent natal females, many with peri-pubertal onset of gender dysphoria, and with high rates of psychiatric and neurodevelopmental comorbidity. A Finnish register study found psychiatric morbidity in 48 percent of adolescents referred between 2011 and 2019, compared with 24 percent before 2011.

Why these numbers rose so sharply remains contested, with proposed explanations ranging from social and peer influence to increased awareness, positive publicity about gender-affirming treatment, and reduced stigma. What is less disputed is how the clinical model changed. Over the past three decades, the gender-affirmative approach—affirming a stated gender identity and offering hormonal and surgical interventions to align the body with it—became the dominant paradigm, endorsed by influential guidelines such as those of the World Professional Association for Transgender Health. Simultaneously, many services moved away from rigorous mental health assessment and strict eligibility criteria, which some advocates framed as unethical gatekeeping. The Dutch protocol that pioneered medical intervention in adolescents originally demanded childhood-onset dysphoria, persistence into adolescence, a supportive environment, absence of serious psychiatric comorbidity, and minimum age thresholds; several of those safeguards, including the childhood-onset criterion, were later dropped from Dutch guidance and are absent from WPATH’s 2022 standards.

Against this shifting landscape, the authors review what is known about detransition—generally defined as stopping or reversing the social, medical, or administrative changes achieved during a gender transition, partially or completely, temporarily or permanently. Prevalence estimates vary widely because of methodological flaws and inconsistent definitions, but a critical review estimated that discontinuation of hormonal medication ranges from 1.9 to 30 percent, with detransition and regret after hormonal treatment ranging from 0 to 10 percent and after surgery from 0 to 2.4 percent. A recent latent class analysis of 957 detransitioned people in the United States and Canada identified four distinct subgroups, only one of which—roughly a third of participants—combined changed gender identity with moderately high regret. The authors stress that most patients do not regret their treatment and that their analysis deliberately excludes positive experiences, focusing solely on the distressed minority.

The conceptual core of the paper is the argument that, for some of these patients, the treatment itself can constitute a traumatic event under DSM-5-TR criteria. The manual requires exposure to actual or threatened death or serious injury, and the authors contend that irreversible physical sequelae of gender-affirming treatment can meet this threshold when viewed through the lens of a patient who has reidentified with their natal sex. These sequelae include both anticipated effects—sterility after gonadectomy, loss of sexual function after vaginoplasty, loss of breastfeeding capacity after mastectomy, voice deepening and male-pattern alopecia with testosterone, breast growth and testicular atrophy with estrogen—and surgical complications such as urinary incontinence, sexual dysfunction, and thromboembolism. For a young person confronting the permanence of these changes, the authors suggest, the injury may be experienced as catastrophic, generating horror, helplessness, anguish, self-disgust, betrayal, rage, guilt, and shame.

Whether such a presentation qualifies as PTSD depends on the full diagnostic picture. The DSM-5-TR requires symptoms from four clusters: intrusive memories, dreams, or dissociative reactions; persistent avoidance of trauma-related reminders; negative alterations in cognition and mood, such as persistent self-blame or distorted beliefs about oneself and the world; and alterations in arousal and reactivity, including hypervigilance, irritability, and sleep disturbance. Symptoms must persist beyond a month, cause clinically significant distress or impairment, and not be attributable to substances or another medical condition. The authors anticipate two objections. First, critics may argue that patients who did not experience the treatment as traumatic at the time cannot later develop PTSD—but the DSM-5-TR explicitly recognizes delayed expression, sometimes emerging years after the event, and the modern criteria no longer require intense peri-traumatic fear. Second, some may argue that consent negates trauma; the authors counter with the observation that a soldier who voluntarily enlists can still develop PTSD, quoting the psychoanalyst Caroline Garland’s remark that an event may be no less traumatic because it was sought out.

To illustrate how attitudes toward a bodily intervention can transform retroactively, the authors draw a carefully hedged parallel with female genital mutilation or cutting. They are explicit that they are not equating the two practices. Rather, they invoke research showing that some women who underwent FGM/C in childhood felt no trauma at the time but developed PTSD symptoms after leaving their cultural context and reinterpreting the procedure through a new framework of belief—a process described as hitting bottom, in which pride and honor give way to shame and mourning. Some detransitioners, the authors propose, undergo an analogous reversal: once embedded in communities that celebrated their transition and praised by their clinicians, they later come to see themselves as people subjected to injurious procedures that damaged fertility or sexual function. Some also report harassment and ostracism from both trans communities and gender clinicians after speaking out, a phenomenon the literature terms detransphobia.

The second pillar of the paper is moral injury, a concept originating in studies of Vietnam War veterans and defined in its original form as a betrayal of what is right, by someone who holds legitimate authority, in a high-stakes situation. In September 2025, the American Psychiatric Association incorporated moral problems into the DSM-5-TR under code Z65.8, expanding the former Religious or Spiritual Problem category so clinicians can now flag moral dilemmas, distress, and injury—experiences that disrupt a person’s sense of right and wrong or their sense of goodness concerning themselves, others, or institutions. The code can stand alone or accompany diagnoses such as PTSD or depression. The authors argue this framework maps closely onto some detransition narratives: patients may feel betrayed by the clinicians who provided treatment, by the profession that regulated it, or by institutions that promoted it, and may experience guilt, shame, loss of trust, and inability to forgive. Notably, the DSM-5-TR itself notes that trauma inflicted by trusted caregivers heightens both the risk and severity of PTSD, and research suggests PTSD complicated by moral injury is harder to treat unless the moral component is explicitly addressed.

The paper closes with clinical and scientific implications rather than treatment protocols, which the authors defer to future work. They note that ICD-11 offers a broader definition of traumatic events and a distinct complex PTSD category requiring severe affect dysregulation, negative self-concept, and interpersonal difficulties, and that when full PTSD criteria are unmet, a specified trauma- and stressor-related disorder may apply. Recovery, they suggest, likely requires more than psychotherapy: acknowledgment of the injury, community understanding and reintegration, shared responsibility, and, for some, social action or legal accountability—a survivor mission that trauma research has linked to resolution. The authors also urge clinicians to examine their own cognitive biases, noting that interventions endorsed by eminent organizations may be assumed to be uniformly beneficial, that misinformation and censorship affect the field, and that high-quality guidelines from Scandinavian health authorities actually recommend psychotherapy and psychosocial support as the primary approach for minors. Above all, they argue, detransitioned people who have shared their stories deserve recognition for drawing attention to a form of iatrogenic harm that, on their account, psychiatry now has the diagnostic vocabulary to name—and the obligation to study.

Subject of Research: Posttraumatic stress disorder and moral injury among individuals who detransition after gender-affirming medical treatment

Article Title: Detransition, Posttraumatic Stress Disorder, and Moral Injury

Article References: Clayton, A., & McDeavitt, K. (2026). Detransition, Posttraumatic Stress Disorder, and Moral Injury. Archives of Sexual Behavior. https://doi.org/10.1007/s10508-026-03669-x

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03669-x

Keywords: detransition, posttraumatic stress disorder, moral injury, gender-affirming treatment, gender dysphoria, DSM-5-TR, trauma, iatrogenic harm, informed consent, pediatric gender medicine, Archives of Sexual Behavior, mental health

The argument rests on a striking mismatch between how clinicians are trained and how most of the world actually experiences sleep. Contemporary sleep medicine remains overwhelmingly grounded in biomedical models that treat sleep as a physiological process to be measured and corrected. Yet the authors point out that dreams, nightmares, sleep paralysis, and nocturnal visions have historically been interpreted through spiritual, moral, existential, and cultural frameworks worldwide. These interpretations are not relics of a distant past. They continue to shape how billions of people understand what happens to them at night, how distressed they become when sleep goes wrong, and whether they ever seek help from a physician at all.

The scale of the phenomenon is difficult to dismiss. According to data cited in the editorial, approximately 75.8 percent of the world’s population is affiliated with a religious tradition, and even among the nearly one-quarter of people who are religiously unaffiliated, many still identify as spiritual or hold spiritual beliefs and practices. Sleep-related spiritual experiences, far from being rare curiosities, appear to be commonplace. In the United States, 53 percent of adults report dreams or other experiences involving deceased loved ones. In Brazil, a nationwide study found that 70 percent of respondents reported at least one precognitive dream. Out-of-body experiences, including those occurring during sleep or in the fragile transitions between sleep and wakefulness, have been reported by up to 20 percent of the population. For many people, these experiences are meaningful or even comforting. But when individuals lack cultural, spiritual, or social frameworks through which to interpret them, the same experiences can become sources of significant distress that drive them into clinics and sleep labs.

Mainstream psychiatry has already begun to respond. In 2025, the American Psychiatric Association expanded the DSM-5-TR code Z65.8, formerly labeled Religious or Spiritual Problem, to Moral, Religious, or Spiritual Problem. The change formally recognizes that moral and spiritual distress or conflict can be clinically significant and worthy of clinical attention, without pathologizing them as psychiatric disorders. The authors argue that this shift is directly relevant to sleep medicine, because a growing body of evidence links trauma exposure, moral injury, and spiritual distress with nightmares, hyperarousal, and insomnia symptoms. A veteran wrestling with moral injury, for example, may present to a sleep clinic with severe nightmares and fragmented sleep, while the underlying spiritual wound remains unexamined and untreated.

Religion and spirituality are also increasingly understood as determinants of sleep health in their own right. The editorial reviews studies reporting associations between religious service attendance, positive religious coping, and spiritual well-being on one hand, and better sleep quality and longer sleep duration on the other. Conversely, religious struggle, spiritual distress, religious doubt, and guilt have been linked to poorer sleep outcomes. The proposed mechanisms are not vague appeals to the ineffable. The authors describe plausible pathways including enhanced coping resources, greater resilience to stress, reduced neural reactivity to negative emotional stimuli, and neurobiological correlates associated with lower risk of depression and improved emotional regulation. In other words, the way people find meaning, hope, and community may leave measurable traces in the stress physiology and emotional regulation systems that govern sleep.

Cross-cultural research makes the case even more vivid. Among Māori communities in Aotearoa, New Zealand, qualitative research conceptualizes sleep as a spiritually significant state in which the wairua, or spirit, awakens, journeys, learns, and provides guidance for waking life. In several African cultural traditions, dreams may be interpreted as sources of guidance, prophecy, or spiritual influence, shaping how sleep disturbances are understood. Among Yoruba communities in Nigeria, nightmares and dreams may be interpreted as manifestations of witchcraft. The authors emphasize that such beliefs are not confined to any single population; they represent a common way that human beings make meaning of sleep. The clinical risk arises when experiences such as sleep paralysis, sensed presence, or vivid dream encounters are interpreted outside their cultural and spiritual context, leading to unnecessary pathologization or stigma.

What would spiritually sensitive sleep care actually look like in practice? The authors are careful to draw boundaries. Integrating spirituality into sleep medicine does not require clinicians to endorse religious beliefs, nor does it involve pastoral counseling or proselytizing. Instead, it involves recognizing when existential, spiritual, or cultural frameworks meaningfully shape a patient’s sleep experiences, distress, coping, and treatment engagement. One practical tool is the spiritual history, a brief evaluation of a patient’s spiritual needs during the clinical encounter, which can surface concerns relevant to sleep health and guide appropriate follow-up and referral. Educational initiatives such as the Interprofessional Spiritual Care Education Curriculum, known as ISPEC, already provide clinicians with training and competencies to address patients’ spiritual needs in clinical settings, and the editorial suggests such frameworks could extend into sleep medicine training.

Institutional models already exist. The Veterans Affairs health system in the United States has integrated spiritual care through interdisciplinary teams that include chaplains, offering support grounded in deep listening, presence, and person-centered care. Related initiatives, such as military spiritual fitness programs, reflect growing institutional recognition that meaning, values, and purpose are components of resilience, well-being, and readiness. Moreover, many patients actively want to discuss spiritual concerns with their clinicians, a preference that current sleep medicine practice rarely accommodates. On the therapeutic side, randomized clinical trials have demonstrated the efficacy of integrating spiritual practices into cognitive-behavioral interventions, suggesting a natural application for behavioral sleep medicine, where cognitive-behavioral therapy for insomnia already stands as the first-line treatment.

The editorial arrives at a moment when medicine more broadly is pivoting toward precision and person-centered care. The National Center for Complementary and Integrative Health has championed Whole Person Health, a biopsychosocial-spiritual framework that recognizes physical, mental, social, and spiritual dimensions of health as interconnected and collectively capable of shaping outcomes relevant to sleep. The authors frame their message as a call to action: greater attention to the influence of religion and spirituality on the practice of sleep medicine could strengthen the therapeutic alliance, improve culturally and spiritually responsive care, and advance more holistic approaches to sleep health. The work was supported in part by the Intramural Research Program of the National Institutes of Health, and the authors note that the findings and conclusions are their own and do not necessarily reflect the views of the NIH.

For a field built on actigraphy, CPAP machines, and sleep diaries, the proposal may seem like an unlikely revolution. But the underlying logic is pragmatic rather than mystical. If three out of four people on Earth belong to a religious tradition, if half of American adults dream of deceased loved ones, and if spiritual distress is now a recognized diagnostic category linked to nightmares and insomnia, then a sleep medicine that ignores these realities is missing information that shapes diagnosis, communication, and treatment adherence. The editorial does not ask clinicians to become theologians. It asks them to ask better questions, to listen for the frameworks through which patients already understand their nights, and to treat the sleeping person not merely as a nervous system to be regulated, but as a whole human being for whom sleep may carry meanings that science is only beginning to measure.

Subject of Research: Integrating spirituality and religion into whole-person, person-centered sleep medicine

Article Title: The need to integrate spirituality into sleep medicine as a dimension of whole-person, person-centered care

Article References: Singh, R., Puchalski, C. M., Moreira-Almeida, A., Merry, B., Rosmarin, D. H., Koenig, H. G., & Jackson, C. L. (2026). The need to integrate spirituality into sleep medicine as a dimension of whole-person, person-centered care. Journal of Clinical Sleep Medicine, 22(1), Article 135. https://doi.org/10.1007/s44470-026-00137-0

Image Credits: AI Generated

DOI: 10.1007/s44470-026-00137-0

Keywords: sleep medicine, spirituality, religion, whole-person care, dreams, sleep paralysis, moral injury, insomnia, spiritual care, DSM-5-TR, cultural psychiatry, person-centered care

Gender dysphoria refers to the psychological distress that arises when a person’s gender identity does not align with their sex observed at birth. For decades, the clinical literature described two broad developmental pathways. The early-onset pathway typically begins in early childhood, is marked by pervasive gender-nonconforming behavior, and is strongly associated with same-sex attraction upon sexual maturity. The late-onset pathway, documented extensively in males, emerges during or after puberty and is historically associated with an absence of childhood gender nonconformity and with attraction to the opposite sex. What has changed dramatically over the past fifteen years is the demographic composition of gender clinics: referrals are now dominated by adolescent females, many of whom report no childhood history of gender-nonconforming behavior and many of whom carry significant comorbid psychiatric diagnoses.

The research team, led by Hollie Hammond of Western Sydney University together with colleagues including James S. Morandini, recruited participants through social media platforms such as Facebook and Reddit, as well as word of mouth, rather than through gender clinics. This non-clinical sampling strategy was a deliberate methodological choice. Most previous evidence on the subject came either from clinic-referred samples or from parent-report studies, the latter of which have drawn sustained criticism for relying exclusively on parental accounts and for allegedly conflating transgender identity with a psychiatric diagnosis. By surveying young people directly, the researchers aimed to capture the experiences of a population that rarely appears in clinical datasets. Participants completed an anonymous online survey lasting an average of 37 minutes, and the team applied quality controls including bot detection, minimum completion times, and manual review of responses.

To classify onset type, participants answered eight symptom items adapted from the DSM-5-TR diagnostic criteria for childhood gender dysphoria, recalling their experiences between the ages of 3 and 11. Those who reported meeting at least six symptoms for a minimum of six months—the DSM-5-TR childhood diagnostic threshold—were placed in the early-onset group; those below the threshold were classified as late-onset. The results were lopsided: 29 participants (21 percent) fell into the early-onset category while 109 (79 percent) were late-onset. The late-onset group reported an average of fewer than two childhood symptoms, with the lowest endorsement on internal items such as dislike of one’s sexual anatomy and desire for the physical attributes of the other sex. The early-onset group, by contrast, most often recalled preferences related to clothing and cross-sex roles in make-believe play.

The statistical contrasts between the groups were substantial. Early-onset participants reported significantly more childhood symptoms, with an effect size of Cohen’s d = 3.05—one of the largest reported in this literature. They also adopted a non-cisgender identity earlier, at an average age of 12.68 years compared with 15.01 years for the late-onset group, and came out to others roughly two years sooner. Yet even the early-onset group diverged from classic clinical cohort studies, in which children typically presented to clinics around age six and socially transitioned by age eight. The authors suggest their early-onset subgroup may better be described as a pseudo-early-onset, or late childhood and early adolescent onset, pathway—one that does not replicate the developmental signature of the historical cohorts on which much of the evidence base for early gender-affirming care was built.

Gender identity itself differentiated the groups. Late-onset participants were significantly more likely to identify as non-binary, whereas early-onset participants more frequently endorsed binary identities such as man or male. The authors report that this association between onset type and gender identity has not been documented before, and they flag it as clinically significant given emerging evidence that non-binary identity is associated with poorer mental health outcomes in some meta-analyses and with higher odds of discontinuing gender-affirming medical treatment in others. Transmasculine was the most commonly endorsed identity in both groups, chosen by more than half of participants in each.

Sexual orientation told a different story from the male-dominated literature. Using an adapted Kinsey scale, over half of participants in both groups—51.7 percent of the early-onset and 57.8 percent of the late-onset—reported at least partial same-sex attraction, and only about a fifth to a quarter described themselves as exclusively attracted to males. This represents a dramatic overrepresentation of same-sex attraction relative to general population estimates, in which roughly nine in ten women are heterosexual. Notably, however, orientation did not differ between onset groups, challenging the traditional male-pattern association in which the early-onset pathway is linked to homosexuality and the late-onset pathway to heterosexuality. The authors suggest that typologies developed for males with gender dysphoria may simply not transfer to females.

The clinical picture also split along onset lines. Early-onset participants reported roughly twice as many diagnosed comorbid psychiatric conditions as their late-onset counterparts, with significantly higher rates of attention-deficit/hyperactivity disorder, generalized anxiety disorder, and panic disorder. They also scored significantly higher on measures of dysphoria severity and anxiety, though the two groups did not differ on depression, stress, or well-being—both scoring in the moderate to extremely severe range across distress measures. The late-onset group was far from symptom-free: 56.9 percent reported at least one comorbid diagnosis, a figure that overlaps with the 62.5 percent reported in the controversial 2018 parent-report study by Lisa Littman that first proposed the rapid-onset gender dysphoria hypothesis. The authors note that this self-reported overlap offers some indirect support for aspects of that hypothesis, while stopping short of endorsing it, given that their data cannot establish whether distress preceded dysphoria or followed it.

Peer relationships offered a notable null finding. Participants reported having approximately two to three transgender or gender-diverse friends before realizing their own dysphoria, with no significant differences between onset groups in the number of friends, online friends, or in-person friends. If the late-onset group were primarily composed of the hypothesized sociogenic cohort driven by peer influence, the authors reason, one would expect them to report more transgender friends prior to onset than the early-onset group. That pattern did not emerge, though the authors caution that the sociogenic hypothesis remains deeply contested and that cross-sectional data cannot resolve questions of causality or directionality.

The study’s limitations are considerable and the authors are candid about them. The sample was a small, uneven convenience sample reliant on retrospective self-report, vulnerable to recall bias—particularly given evidence that distressed and neurotic individuals tend to overestimate negative childhood affect, which could inflate early-onset classification among the most distressed participants. A longitudinal design tracking children from early development into adulthood, ideally with corroboration from parents, teachers, or clinicians, would be needed to firmly establish the temporal relationships the present data can only suggest. Still, the authors argue the findings carry practical implications: clinicians should take careful histories of symptom onset, since onset type may signal different comorbidity profiles, different dysphoria severity, and even different identity trajectories. Most adolescent females experiencing gender dysphoria, the study concludes, report little childhood gender nonconformity, and the assumption that historical clinical patterns observed mostly in males apply to today’s predominantly female adolescent population is one that future research can no longer afford to make.

Subject of Research: Early- versus late-onset gender dysphoria in adolescent and young adult birth-assigned females

Article Title: Demographic and Clinical Features of Early-Onset versus Late-Onset Gender Dysphoria Among Adolescent and Young Adult Birth-Assigned Females

Article References: Hammond, H., Walter, E., Daly, M., Morandini, J. S., & Smith, E. (2026). Demographic and Clinical Features of Early-Onset versus Late-Onset Gender Dysphoria Among Adolescent and Young Adult Birth-Assigned Females. Archives of Sexual Behavior. https://doi.org/10.1007/s10508-026-03571-6

Image Credits: AI Generated

DOI: 10.1007/s10508-026-03571-6

Keywords: gender dysphoria, adolescents, late-onset, early-onset, non-binary, transgender, mental health, DSM-5-TR, sexual orientation, comorbidity, Archives of Sexual Behavior, birth-assigned females

The research, led by Mahrukh Shakoor and Tamim Ahmed Khan of the Department of Software Engineering at Bahria University in Islamabad, together with Hina Ghafoor of the university’s Department of Professional Psychology, describes a system that integrates reflective listening, large language models, knowledge graph reasoning, and clinical guidelines drawn from the DSM-5-TR, the current edition of the Diagnostic and Statistical Manual of Mental Disorders. The goal is not to replace clinicians but to give them a structured, interpretable tool for the initial assessment process, where human resources are scarce and early signals are easy to miss.

At the heart of the framework is a two-stage screening workflow designed to mimic, in a limited way, the flow of a real clinical intake. In the first stage, a reflective-listening conversational agent engages the user in empathetic dialogue. Reflective listening is a counseling technique in which the listener mirrors and restates what the speaker has said, validating their experience and encouraging deeper disclosure. By building this technique into the language model, the researchers aimed to create an agent that does not simply interrogate the user with checklist questions but instead draws out clinically relevant symptoms through conversation that feels supportive and engaging.

During this first-stage dialogue, the system performs contextual reasoning over what the user says, extracting symptom features and representing them within a knowledge graph, a structured network of entities and relationships that allows the AI to connect scattered conversational cues into a coherent clinical picture. Rather than treating each mention of sleeplessness, worry, or low mood as an isolated data point, the knowledge graph links symptoms to one another and to diagnostic categories, enabling an initial mental health risk screening that can be traced and explained. This emphasis on explainability is central to the framework: clinicians can see how the system arrived at its conclusions rather than receiving an opaque risk score from a black-box model.

The second stage brings quantitative rigor to the conversation. The framework incorporates standardized psychometric instruments, including the Penn State Worry Questionnaire, a widely used measure of pathological worry; the Beck Depression Inventory-II, one of the most established tools for assessing the severity of depressive symptoms; and the McLean Screening Instrument for Borderline Personality Disorder, a validated screening tool for that condition. By embedding these instruments into the workflow, the system grounds its conversational impressions in validated, disorder-specific measures, producing a quantitative assessment that supports more fine-grained risk estimation for particular conditions.

To train and evaluate the system, the team constructed a structured dataset that is notable for its clinical grounding. It comprises AI-generated reflective conversations, screening labels informed by DSM-5-TR criteria, extracted symptom features, knowledge graph representations, and, critically, annotations reviewed by clinicians. This dataset was then used to fine-tune the proposed language model, aligning its behavior not just with general linguistic patterns but with the specific vocabulary and logic of mental health assessment. The clinician-reviewed component of the dataset provides a human benchmark against which the AI’s outputs can be measured.

Experimental results reported in the study show encouraging agreement between the framework’s AI-assisted screening outcomes and assessments reviewed by clinicians. The authors highlight that integrating reflective listening, psychometric evaluation, knowledge graph reasoning, and generative AI improved not only screening performance but also interpretability and user engagement, two factors that often pull in opposite directions in automated health tools. Systems that are highly engaging are frequently opaque, while systems that are transparent can feel clinical and cold. The reflective-listening approach appears to soften that trade-off, keeping users in conversation while the underlying reasoning remains inspectable.

The ethical scaffolding around the study is unusually detailed for work in this space. The research was reviewed and approved by the Ethics Review Committee of Bahria School of Professional Psychology at Bahria University, with approval dated 26 January 2026, and all procedures were conducted in accordance with the Declaration of Helsinki. Participation was voluntary and informed consent was obtained from all participants, with data anonymized before analysis and personally identifiable information removed. Importantly, the framework was evaluated solely as an AI-assisted screening and decision-support prototype and was not used for clinical diagnosis or treatment. Participants identified as potentially high-risk, including those expressing suicidal ideation, self-harm, psychotic symptoms, or abuse-related concerns, were advised to discontinue the assessment and seek immediate evaluation from qualified mental health professionals or emergency services, a safeguard that acknowledges the real dangers of deploying conversational AI in sensitive domains.

The significance of the work lies in how it addresses the structural bottlenecks of mental healthcare. Screening is the first and often most neglected step in the care pathway: a person must be identified as needing help before any treatment can begin. In many parts of the world, the ratio of mental health professionals to population is so low that systematic early screening is simply impossible at scale. A conversational agent that can conduct an empathetic intake, administer validated psychometric measures, and hand clinicians a structured, explainable summary of risk could extend the reach of overstretched services, flagging individuals who would otherwise remain invisible until crisis point.

The researchers are careful to position the framework as a support tool rather than an autonomous diagnostician. The system’s outputs are intended to assist healthcare professionals during initial assessment, with final clinical judgment remaining in human hands. That framing, combined with the framework’s explainability features and its grounding in DSM-5-TR criteria and validated instruments, reflects a growing consensus in medical AI research: the most promising systems are not those that attempt to replace clinical expertise, but those that structure information, surface early signals, and make their reasoning transparent enough for professionals to trust, verify, and act upon. As generative AI continues to mature, studies like this one suggest a future in which the first conversation a struggling person has about their mental health, often the hardest one to start, might be made a little easier, and a great deal more likely to lead to help.

Subject of Research: An explainable AI framework for early mental health screening and clinical decision support using reflective listening and generative AI

Article Title: An explainable AI framework for early mental health screening and clinical decision support using reflective listening and generative AI

Article References: Shakoor, M., Khan, T. A., & Ghafoor, H. (2026). An explainable AI framework for early mental health screening and clinical decision support using reflective listening and generative AI. Discover Psychology. https://doi.org/10.1007/s44202-026-00877-3

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00877-3

Keywords: mental health screening, explainable AI, reflective listening, generative AI, large language models, knowledge graph reasoning, DSM-5-TR, clinical decision support, psychometric assessment, psychological disorders, early identification, conversational agents

The study cohort comprised 4,250 adults diagnosed under DSM-5-TR criteria, including 1,515 with bipolar disorder—839 with bipolar I and 676 with bipolar II—and 2,735 with major depressive disorder. Patients had been ill for an average of 14.1 years and were followed prospectively and systematically for 4.73 years, giving the investigators an unusually rich longitudinal window into the relationship between substance misuse and the course of mood illness. Across the full sample, the overall prevalence of lifetime substance abuse averaged 11.5 percent, but that single figure conceals dramatic differences between diagnostic groups and between the sexes.

The headline comparison is unambiguous: substance abuse was 4.36 times more prevalent among patients with bipolar disorder than among those with major depressive disorder, affecting 22.8 percent of the bipolar group versus just 5.23 percent of the depressed group. Within the bipolar spectrum, the gradient continued. Bipolar I patients showed a lifetime substance abuse rate of 26.6 percent, roughly 1.45 times the 18.4 percent observed in bipolar II patients. This pattern reinforces a growing body of evidence that risk of comorbid substance misuse scales with the severity and mania load of the mood syndrome, rather than being a uniform feature of mood disorders in general.

Gender emerged as one of the most powerful risk factors in the entire dataset. Among the 1,583 men studied, 20.2 percent had a lifetime substance abuse history, compared with only 6.37 percent of the 2,667 women—a 3.17-fold difference that dwarfs the gender gaps typically reported in general population surveys. The authors suggest this pronounced male excess may interact with the biological and social vulnerabilities specific to mood disorders, compounding a baseline gender difference in substance abuse risk that exists even in people without psychiatric illness.

Equally striking was the finding on polyabuse, defined as misuse of multiple substances. Polyabuse averaged 6.35 percent across the cohort overall, but it was 5.60 times more common in bipolar disorder, where 13.5 percent of patients reported it, than in major depression, where the rate was 2.41 percent. When the researchers ranked specific abused substances, alcohol dominated at 20.9 percent, followed by polyabuse at 6.35 percent, cannabis at 3.29 percent, with opioids and stimulants each at 0.31 percent and benzodiazepines at a negligible 0.02 percent. For every category, rates were higher in bipolar disorder than in major depressive disorder, with alcohol and multi-substance misuse driving the overwhelming majority of the comorbidity burden.

Beyond diagnosis and sex, the team mapped a detailed clinical and psychosocial signature of patients prone to substance abuse. Those with lifetime substance misuse were more likely to be male, less likely to be married, more likely to be divorced, had fewer children, and were more often unemployed. They smoked more, experienced an earlier onset of mood illness, and carried more psychiatric comorbidity—although notably less somatic or medical comorbidity—than their peers without substance problems. Most sobering of all, substance-abusing patients showed markedly more suicidal behavior, a finding that echoes prior meta-analytic work linking co-occurring bipolar and substance use disorders to dramatically elevated suicide attempt risk.

The study also probed temperament, using structured affective temperament ratings, and found that an irritable temperament was associated with lifetime substance abuse. Intriguingly, however, substance abuse was not linked to greater affective morbidity—that is, substance-abusing patients did not simply accumulate more mood episodes. This dissociation is scientifically important because it argues against the simplistic interpretation that patients abuse substances merely in proportion to how often their mood illness relapses. Instead, the data point toward trait-like vulnerabilities, including temperament, impulsivity-related traits, and early-onset illness, as the more proximal drivers of substance misuse in this population.

To isolate the factors that independently predict substance abuse, the researchers applied multivariable regression analysis. The ranked order of independently associated factors was: smoking, male gender, younger age at illness onset, a bipolar diagnosis, never having married, an irritable temperament, and having fewer children. Cigarette smoking topping the list is particularly noteworthy from a mechanistic standpoint; smoking may serve as both a marker of broader impulsivity and reward-system dysregulation and as a gateway behavior that facilitates progression to other substance misuse. The prominence of early onset likewise suggests that a neurodevelopmentally earlier form of mood illness carries heightened vulnerability to addictive comorbidity, consistent with shared genetic liability hypotheses supported by recent twin, family, and population-based studies.

From a clinical standpoint, the implications are concrete. Because alcohol accounts for the large majority of abused substances in this population, routine screening for alcohol misuse should be considered standard practice in mood disorder clinics, with particularly vigilant surveillance of male patients, smokers, and those with early-onset or bipolar I illness. The strong association with suicidal behavior argues that dual-diagnosis patients warrant intensified safety planning and may benefit from integrated treatment models that address mood and substance problems simultaneously rather than sequentially. Previous research has shown, for example, that comorbid substance use disorder can impair recovery from depression during standard antidepressant treatment and reduce responsiveness to mood stabilizers in bipolar patients, underscoring the cost of ignoring the comorbidity.

The study’s scale and prospective design distinguish it from much of the existing literature, which has relied heavily on cross-sectional surveys, meta-analyses of heterogeneous samples, or national registries that lack detailed clinical characterization. By directly comparing bipolar I, bipolar II, and major depressive disorder within a single, systematically assessed cohort followed for years, the McLean-Harvard team has provided what may be the most granular stratification of substance abuse risk across the major mood disorders to date. As substance-related disorders continue to impose an enormous global disease burden, and as rates of cannabis and other drug use climb among young adults, identifying which mood disorder patients are at highest risk—and intervening early—could pay substantial dividends in reducing disability, hospitalization, and suicide in this vulnerable population.

The diagnostic framework used in the study deserves some attention. By applying DSM-5-TR criteria uniformly across all 4,250 participants, the investigators reduced the diagnostic heterogeneity that has plagued earlier comparisons of substance misuse across mood disorders. This matters because prevalence estimates for comorbid substance use disorders have varied enormously in prior research—sometimes ranging from under 10 percent to over 50 percent in bipolar samples—depending largely on whether studies relied on self-report, registry data, or structured diagnostic interviews, and on whether abuse and dependence were distinguished from mere use.

The prospective element of the design also strengthens causal interpretation in one specific respect. Because participants were followed systematically for nearly five years after baseline characterization, the researchers could examine whether substance abuse predicted subsequent affective morbidity. The finding that it did not—that substance-abusing patients did not experience more mood episodes over time—challenges the widespread self-medication hypothesis in its simplest form. That hypothesis holds that patients turn to alcohol or drugs to dampen painful mood symptoms, implying that heavier substance use should track with more frequent or severe episodes. While self-medication may still operate in individual cases, and prior work has shown that drinking to relieve mood symptoms does predict later alcohol dependence, the present data suggest that in this cohort, trait vulnerabilities rather than episode frequency best explain who develops substance problems.

The near-absence of benzodiazepine misuse, at just 0.02 percent, is a notable detail given clinical concerns about tranquilizer dependence in psychiatric populations. It may reflect the specific composition and treatment setting of the cohort, or genuine patterns of preference for alcohol and cannabis among mood disorder patients, but it contrasts with population surveys in the United States that have documented substantial rates of prescription benzodiazepine misuse. Similarly, the low rates of opioid and stimulant misuse may partly reflect the era and region in which participants were recruited, reminding readers that substance availability and local drug markets shape comorbidity patterns as much as underlying psychopathology does.

Finally, the temperamental finding invites further research. Irritable temperament, measured with validated self-report instruments derived from the Akiskal temperamental framework, has previously been linked to impulsivity and interpersonal conflict, both plausible pathways into substance misuse. If replicated, temperament assessment could become a low-cost screening tool, allowing clinicians to flag newly diagnosed mood disorder patients—especially young men with early-onset illness and a smoking history—for early preventive counseling before problematic use takes hold.

Subject of Research: Lifetime substance abuse prevalence and clinical correlates in bipolar I, bipolar II, and major depressive disorder patients

Article Title: Lifetime Substance Use Disorder in 4250 Bipolar and Major Depressive Disorder Patients

Article References: Miola, A., Tondo, L., & Baldessarini, R. J. (2026). Lifetime Substance Use Disorder in 4250 Bipolar and Major Depressive Disorder Patients. International Journal of Mental Health and Addiction. https://doi.org/10.1007/s11469-026-01718-z

Image Credits: AI Generated

DOI: 10.1007/s11469-026-01718-z

Keywords: bipolar disorder, major depressive disorder, substance abuse, alcohol abuse, polyabuse, comorbidity, suicidal behavior, smoking, affective temperament, dual diagnosis, psychiatry, DSM-5-TR