The review, conducted by Xiao-xiao Li, Zhen-peng Huang, and Kong-jin Quan of the Faculty of Nursing at Guangxi University of Chinese Medicine, casts a wide net. The team searched eight English- and Chinese-language databases from their inception through October 2025, screening 10,818 records. From that mountain of literature, 34 studies met the inclusion criteria, covering the development, evaluation, or cross-cultural validation of instruments intended to detect somatic symptom disorder or closely related constructs. The work follows the PRISMA-ScR reporting guidelines, the accepted standard for scoping reviews, and the authors independently mapped each instrument’s content against a prespecified framework derived from the DSM-5.

That framework is where the technical heart of the review lies. The DSM-5 definition of somatic symptom disorder rests on two pillars. Criterion A requires the presence of one or more somatic symptoms that are distressing or disruptive to daily life. Criterion B captures the psychological and behavioral response to those symptoms, broken into three components: disproportionate thoughts about their seriousness, persistently high anxiety about health or symptoms, and excessive time and energy devoted to symptoms. Under the older DSM-IV framework, the diagnosis of somatoform disorders hinged largely on unexplained symptoms; the 2013 revision shifted the emphasis toward how patients think, feel, and behave in relation to symptoms, regardless of whether a medical explanation exists. Any screening tool built before that shift, or built for a different purpose, may therefore be measuring something subtly but importantly different.

When Li and colleagues mapped the nineteen instruments against these criteria, the mismatch became stark. Ten instruments covered Criterion A only, meaning they quantify somatic symptom burden but say nothing about the maladaptive thoughts, anxiety, and behavioral investment that define the disorder. Four covered Criterion B only, probing the psychological response without establishing that distressing symptoms are present. Five instruments covered both Criteria A and B, and only those, the authors argue, align closely enough with the DSM-5 construct to serve as genuine screening tools for the disorder. Criterion C, which concerns the persistence of symptoms, and the final diagnostic confirmation, remain outside the reach of any brief questionnaire and require clinical assessment.

The instrument landscape the review assembles reads like a history of somatic symptom measurement. Legacy tools such as the Patient Health Questionnaire-15, the Somatic Symptom Scale-8, the Screening for Somatoform Symptoms-7, the Symptom Checklist-90, and the Whiteley Index in its revised seven- and eight-item forms were all built to count or characterize physical complaints, or to capture health anxiety, under pre-DSM-5 constructs. Newer, disorder-specific measures include the Somatic Symptom Disorder-B Criteria Scale, known as SSD-12, which was purpose-built to assess the Criterion B psychological features, and instruments such as the Somatic Symptoms Experiences Questionnaire and the Somatic Symptom Scale-China that emerged more recently in response to the revised diagnosis. The SSD-12 stands out in the evidence base: of the six instruments that underwent any cross-cultural validation, the majority of that work concentrated on the SSD-12, which has been translated and tested in numerous languages and clinical populations.

Psychometric evidence varied widely across the 34 studies. The review summarized internal consistency, test-retest reliability, and structural validity, with factor analytic work employing exploratory and confirmatory factor analysis and reporting fit indices such as the comparative fit index, the Tucker-Lewis index, the root mean square error of approximation, and the standardized root mean square residual. Some instruments showed strong and replicable factor structures; others were evaluated only in single studies, in narrow populations, or with methods that would not meet contemporary standards such as item response theory. The authors note that only eight of the nineteen instruments had any evidence of criterion validity or diagnostic validity, typically expressed as sensitivity, specificity, or the area under the receiver operating characteristic curve against a structured clinical interview such as the SCID-5 Research Version. For the remaining instruments, clinicians and researchers are essentially trusting face validity.

This gap matters in practice. A physician using a Criterion A-only tool, such as a symptom-count checklist, may correctly identify a patient burdened by physical complaints but cannot distinguish a patient with somatic symptom disorder from one with a well-understood medical illness that happens to be painful or fatiguing. Conversely, a Criterion B-only measure may flag pathological health anxiety in someone whose physical symptoms are minimal, a presentation the DSM-5 definition would not capture. The review’s central strategic conclusion is that instruments spanning both criteria are the ones most closely aligned with true screening for the disorder, while everything downstream, including attention to Criterion C and the exclusion of better alternative explanations, belongs in the clinical interview. Screening, in other words, can open the door, but only a clinician can walk through it.

The cross-cultural findings carry particular weight for global mental health. Somatic presentation of psychological distress is common in many cultures, and instruments developed in German, Dutch, or English-speaking settings embed assumptions about how patients conceptualize their bodies and their worries. The review found that only six instruments had undergone cross-cultural validation at all, and that the evidence was heavily concentrated on the SSD-12, with Chinese-language adaptations, including the Somatic Symptom Scale-China, forming a notable share of the literature. The inclusion of Chinese-language databases such as CNKI reflects a deliberate effort by the authors to capture validation work that Western reviews have often overlooked. Even so, the authors conclude that further DSM-5-based diagnostic accuracy studies and cross-cultural validations are needed in intended populations and real clinical settings, rather than in the student and convenience samples that dominate psychometric research.

For clinicians, the practical takeaway is to know what the questionnaire in the waiting room is actually measuring. A brief symptom checklist may be an excellent triage tool for symptom burden and treatment monitoring, but it is not a somatic symptom disorder screen. Instruments that explicitly assess both the presence of distressing symptoms and the disproportionate thoughts, anxiety, and time investment surrounding them offer the closest available approximation to the DSM-5 construct, and the SSD-12 currently carries the strongest and most internationally replicated evidence base. For researchers, the review identifies clear priorities: head-to-head diagnostic accuracy studies against structured clinical interviews, validation in primary care and medical settings where these patients actually present, and rigorous translation and adaptation work for the many languages and health systems where no validated tool exists.

The broader significance of the review extends beyond one diagnosis. It is a case study in what happens when diagnostic criteria change faster than measurement science. The DSM-5 redefined somatic symptom disorder around a psychological-behavioral response to symptoms, yet sixteen of the nineteen available instruments were designed for adjacent constructs under older frameworks, from health anxiety to unexplained symptom counts. Until the measurement toolkit catches up, the review suggests, the field will continue to rely on instruments that illuminate parts of the disorder while leaving its defining features in shadow, and the diagnosis will remain as challenging to detect in routine practice as it has been to define in the taxonomy.

Subject of Research: Development, validation, and cross-cultural adaptation of screening instruments for somatic symptom disorder

Article Title: Screening instruments for somatic symptom disorder: a scoping review of development, validation, and cross-cultural adaptation

Article References: Li, X.-X., Huang, Z.-P., & Quan, K.-J. (2026). Screening instruments for somatic symptom disorder: a scoping review of development, validation, and cross-cultural adaptation. BMC Psychology. https://doi.org/10.1186/s40359-026-05691-z

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05691-z

Keywords: somatic symptom disorder, DSM-5, screening instruments, psychometrics, SSD-12, scoping review, cross-cultural validation, diagnostic accuracy, somatic symptoms, clinical assessment, questionnaires, BMC Psychology

Patient segmentation, the practice of dividing a patient population into groups with similar needs, has become a cornerstone of modern health service planning. Most current approaches rely on register data: diagnoses, medication purchases, hospital admissions, and cost histories crunched by statistical or machine-learning models. These methods are powerful, but they see only the administrative shadow of a patient’s life. What they cannot easily capture is how a person actually copes day to day — whether they feel enabled to manage their own health, whether their values and functional ability align with the care they receive, and how they experience their own health status. Navigator, developed in Finland, was designed to fill precisely that gap by asking patients and the professionals who care for them directly.

The service works through paired questionnaires. Patients answer questions covering their individual values, mood, and ability to function in everyday life, while nurses and other professionals assess the patient’s health status and how well current care is fulfilling the patient’s needs. Because Navigator draws on this human-reported information, its developers argue it can supplement data-driven segmentation with a richer, more personal picture of each individual. Until now, however, a fundamental question hung over the tool: did it actually measure what it claimed to measure? That question — the issue of validity — is what the research team led by Riikka Riihimies of Tampere University set out to answer.

The study took place at Valkeakoski Health Center in Finland, where sixteen nurses used Navigator with 304 diabetic patients receiving primary care. Diabetes was a deliberate choice of study population: people living with diabetes typically have multiple interacting health concerns, long-term relationships with primary care services, and wide variation in how well they cope with daily self-management, making them an ideal group for testing whether a segmentation tool can distinguish meaningful differences between individuals. The study received ethical approval from Tampere University Hospital’s Ethics Committee, and all participants gave written informed consent.

The researchers ran two complementary tests. The first, construct validity, asked whether Navigator’s internal structure matched its theoretical design. Using exploratory factor analysis, a statistical technique that searches for hidden patterns underlying responses to a set of questions, the team examined whether the questionnaire items clustered into the dimensions Navigator was built around. The second test, concurrent validity, asked whether Navigator’s scores moved in step with established, well-validated instruments measuring similar things. If Navigator’s functional ability dimension correlated strongly with a recognized disability measure, for example, that would be evidence the tool is capturing something real.

The factor analyses delivered encouraging news for the tool’s designers. In the patient questionnaire, the analysis identified two coherent factors, which the researchers labeled the patient’s mood and the patient’s enablement — the sense of being equipped and empowered to manage one’s own situation. In the professional questionnaire, two further factors emerged: fulfilling care, reflecting how well the patient’s needs are being met by current services, and the patient’s health status. These four factors lined up neatly with the dimensions Navigator was intended to measure, providing statistical confirmation that the questionnaires hang together in a theoretically sensible way rather than producing noise.

The construct validity checks extended beyond internal structure. The team derived factor scores and tested specific hypotheses about how different groups should respond. They predicted that men and women would show only small differences in their Navigator responses, and that patients aged 75 and older would differ markedly from younger patients on dimensions tied to enablement, fulfilling care, and health status. Both predictions were largely borne out: gender differences were modest, while age-group differences were significant on most factor scores. A tool that behaves exactly as theory says it should, in the directions theory says it should, gains credibility as a genuine measuring instrument rather than an arbitrary scoring exercise.

The concurrent validity results were equally substantive. Navigator’s functional ability dimension correlated strongly and significantly with four established patient-reported measures: the World Health Organization Disability Assessment Schedule 2.0, a comprehensive disability instrument; self-rated health; the EuroQol Visual Analog Scale, which captures health-related quality of life on a single scale; and a twelve-item well-being questionnaire. All of these correlations were highly significant. Meanwhile, the professional-assessed health status dimension correlated significantly with objective clinical indicators, including the number of chronic conditions the patient reported, the total amount of medication used, glycated hemoglobin reflecting blood sugar control, the urine albumin-to-creatinine ratio signaling kidney involvement, smoking status, and body-mass index. In other words, what patients and nurses reported through Navigator tracked closely with both validated subjective measures and hard clinical data.

For clinicians and health service managers, the significance of these findings lies in what Navigator could add to existing segmentation practice. Data-based methods excel at identifying high-cost, high-need patients from administrative records, but they are blind to the personal experience of illness. A patient whose records look moderate on paper may in fact be struggling badly with daily life, while another with an intimidating diagnosis list may be coping confidently and needs little extra support. By adding structured information about mood, enablement, functional ability, and how well care is fulfilling individual needs, Navigator offers a way to refine the groups produced by register-based algorithms and to direct resources with finer granularity. The authors conclude that the tool appears valid for measuring patients’ coping in everyday life and health status, and may therefore supplement data-based segmentation by deepening understanding of the individuals within patient groups.

Cautions remain, as they do with any validation study. The research was cross-sectional, capturing a single moment in time rather than following patients over months and years, and it involved a single Finnish health center and a specific population of diabetic patients. Whether Navigator performs equally well in other patient groups, other regions, and other health system contexts will require further study, as will evidence that using the tool actually improves outcomes or reduces costs in practice. Still, the study marks an important step for a tool built on a simple premise: that the best way to understand how patients are coping is to ask them, in a structured and scientifically validated way, and to pair their answers with the observations of the professionals who know their care. As health systems everywhere search for ways to personalize care without exploding budgets, instruments like Navigator — now with evidence behind them — may prove that the human voice belongs at the heart of population health analytics.

Subject of Research: Validation of the Navigator patient segmentation service for diabetic patients in Finnish primary care

Article Title: Construct and concurrent validity of the Navigator patient segmentation service: cross-sectional study among diabetic patients in Finnish primary care

Article References: Riihimies, R., Tolvanen, E., Helminen, M., Kosunen, E., & Koskela, T. (2026). Construct and concurrent validity of the Navigator patient segmentation service: cross-sectional study among diabetic patients in Finnish primary care. BMC Health Services Research. https://doi.org/10.1186/s12913-026-15684-8

Image Credits: AI Generated

DOI: 10.1186/s12913-026-15684-8

Keywords: patient segmentation, Navigator, diabetes, primary care, construct validity, concurrent validity, exploratory factor analysis, Finland, health services research, functional ability, questionnaires, eHealth

The technical core of the underlying study is deceptively simple. Kambeitz and colleagues analyzed multiple large-scale datasets and computed the empirical associations between individual questionnaire items, essentially asking which questions tend to be answered in similar ways by the same people. They then turned to large language models, which represent words and sentences as high-dimensional numerical vectors known as embeddings, capturing semantic and sentiment information learned from vast corpora of text. Using these embeddings, the team generated predicted item-to-item associations based purely on how the items read, rather than on how anyone actually responded to them. Random forest models trained on these linguistic features predicted the empirically observed associations with moderate to high accuracy, and the researchers partially reconstructed the clustering of items into subscales and subdomains across established psychopathology questionnaires.

The implications ripple far beyond a methodological curiosity. If the correlation matrices and factor structures that underpin decades of psychiatric measurement can be partially recovered from semantics alone, then the low-dimensional structure traditionally attributed to latent psychopathological traits is, to a non-trivial extent, reflected in the linguistic properties of the measuring instruments themselves. Symptoms, after all, are described verbally. Questionnaires are composed of linguistic items. The covariance structures that emerge from patient responses are responses to those descriptions, not to trait-free signals. Language, in this view, is not a transparent medium through which psychopathology is expressed; it is an active participant in how that psychopathology is operationalized and measured.

Raballo and his colleagues push the argument further, contending that these findings invite a semantic account of measurement itself. Classical psychometrics, as codified in foundational work on conceptual issues in the field, treats items as fallible indicators of latent variables: the item is a symptom proxy, the trait is the real thing, and the correlation between items reflects their shared dependence on that trait. A semantic account inverts part of this logic. If the empirical structure of an instrument can be predicted from its language, then the meaning carried by the items is not incidental noise around a latent signal. It is part of what constitutes the measurement. The instrument does not merely read out a trait; it frames, filters, and partly generates the structure that the trait model then claims to discover.

Convergent evidence from other recent research supports this reframing. Independent work published in Scientific Reports and in the proceedings of the tenth Workshop on Computational Linguistics and Clinical Psychology has examined how generative language models and semantic embeddings relate to psychometric data, finding that semantic properties of items carry measurable information about their empirical behavior. Studies of semantic psychometrics have similarly suggested that questionnaire meaning and questionnaire statistics are deeply intertwined. Meanwhile, methodological critiques published in Nature Human Behaviour have scrutinized how language models should be evaluated and used in research, adding cautionary context: embeddings capture regularities of text, and those regularities may reflect cultural, historical, and clinical conventions as much as they reflect the structure of mental life.

That caveat matters, and it points to one of the most philosophically interesting aspects of the debate. Psychopathology is described in clinical language that has evolved over more than a century, shaped by diagnostic manuals, textbook traditions, and the vocabulary of distress available to patients and clinicians alike. When a large language model learns that items about low mood, anhedonia, and fatigue cluster together, it may be learning something about depression as a construct, but it may equally be learning something about how depression is conventionally written about. Disentangling these possibilities is now an urgent empirical task. The correspondence between semantic embeddings and empirical item associations could reflect genuine psychopathological structure, shared semantic conventions, or a feedback loop in which clinical language shaped the instruments, the instruments shaped the data, and the data shaped the constructs.

The epistemological stakes are considerable. Psychiatric diagnosis has spent decades wrestling with the reliability and validity of its categories, and the field’s reliance on self-report questionnaires has often been defended on the grounds that these instruments operationalize constructs with known psychometric properties. If a meaningful portion of those properties can be derived from the wording of the items without consulting any respondent data, then the boundary between construct and measurement becomes porous. Researchers designing a new scale might, in principle, use language models to anticipate its factor structure before a single participant completes it. That prospect is both powerful and unsettling. It could accelerate instrument development and flag redundancies before costly data collection. It could also entrench the conventions already embedded in clinical language, making it harder for genuinely new ways of describing suffering to gain empirical traction.

There are also practical opportunities that the commentary’s authors see as within reach. If semantic embeddings predict item associations, then assessment tools of the future might be designed with explicit attention to their linguistic architecture, treating item wording as a design variable with measurable consequences rather than a matter of clinical taste. Language models could help identify which items are semantically redundant, which subdomains are artifacts of phrasing, and which constructs are underrepresented in the existing lexicon of psychiatric measurement. Combined with advances in computational linguistics applied to clinical settings, this could open a path toward instruments that are more transparent about the role their language plays, and possibly toward assessment approaches that draw on natural patient speech rather than fixed questionnaires, provided the measurement properties of such approaches can be established with the same rigor.

Skeptics will rightly note the limits of the current evidence. The reconstruction reported by Kambeitz and colleagues is partial, not complete. Predicting item associations from embeddings does not yet amount to reproducing the full empirical structure of psychopathology, and accuracy that is moderate to high in statistical terms still leaves substantial variance unexplained. Embedding-based predictions also inherit the biases of the text corpora on which language models are trained, including imbalances across languages, cultures, and diagnostic traditions. The commentary in Nature Mental Health does not claim that latent traits are illusions; rather, it argues that the representational correspondence between language and empirical structure has broader implications than the original study articulated, particularly for how measurement is conceptualized and how future tools are built.

What makes this debate resonate beyond specialist circles is the reversal it performs on a familiar anxiety. Much public discussion of large language models in mental health has focused on chatbots as therapists or as sources of clinical advice, with attendant worries about safety and oversight. This line of research points somewhere quieter and perhaps more consequential: language models as instruments for interrogating the instruments of psychiatry itself. The questionnaires that quietly structure diagnosis, treatment trials, and epidemiological statistics turn out to have a semantic skeleton that machines can now see. Making that skeleton explicit, and deciding what it means, may shape how the field measures the mind for years to come. The work by Raballo, Poletti, and Preti, published in September 2026, marks a clear call for that conversation to begin in earnest.

Subject of Research: Semantic structure and measurement in large language models applied to psychopathology and psychometrics

Article Title: Semantic structure and measurement in large language models for psychopathology and psychometrics

Article References: Raballo, A., Poletti, M., & Preti, A. (2026). Semantic structure and measurement in large language models for psychopathology and psychometrics. Nature Mental Health. https://doi.org/10.1038/s44220-026-00712-7

Image Credits: AI Generated

DOI: 10.1038/s44220-026-00712-7

Keywords: large language models, psychometrics, psychopathology, psychiatric assessment, semantic embeddings, questionnaires, latent traits, measurement theory, Nature Mental Health, computational linguistics, clinical psychology, random forest

The study, led by Kristina Tomra Nielsen and colleagues as part of the Danish Intentional Relationship Model research program, set out to evaluate the clinical utility of three Danish-language questionnaires known collectively as the D-CAMQs. These instruments are adapted from the Clinical Assessment of Modes, developed by Renee Taylor to operationalize her Intentional Relationship Model. That model proposes that therapists draw on six distinct interpersonal communication styles, called therapeutic modes: Advocating, Collaborating, Empathizing, Encouraging, Instructing and Problem-solving. Advocating means helping clients access community resources; Collaborating shifts decision-making power to the client; Empathizing involves non-judgmental validation of thoughts and feelings; Encouraging instills hope and courage; Instructing structures, teaches and provides feedback; and Problem-solving guides clients through strategic reasoning about options. According to the model, no single mode is superior, and skilled therapists shift fluidly between them depending on what each client needs in the moment.

To capture how these modes play out in real therapy, the Clinical Assessment of Modes comes in three parts. Before therapy begins, clients complete the CAM-C1, rating how important each mode is to them on a five-point importance scale. After therapy concludes, clients fill in the CAM-C2, reporting how frequently they perceived their therapist actually using each mode. In parallel, the therapist completes the CAM-T, describing their own perception of how often they used each mode with that specific client. The Danish research team had previously translated and culturally adapted all three questionnaires following international guidelines for patient-reported outcome measures, establishing face validity and cultural relevance. What remained unknown was whether the instruments would function usefully in day-to-day clinical practice, not just in the psychometric laboratory.

Clinical utility, in this context, is a four-dimensional concept drawn from Andrew Smart’s multi-dimensional model. Appropriateness asks whether the instrument is relevant and improves clinical decision-making. Accessibility weighs the resources required, especially time, against the benefits gained. Practicability concerns whether instructions and functionality work smoothly, and acceptability addresses ethical, social and psychological concerns around use. The Danish team designed parallel surveys for clients and therapists, each mapped onto these four dimensions, and embedded the questionnaires into genuine therapy courses rather than staged research encounters. Clients completed their pre-therapy questionnaire at the first session, and therapists used the clients’ stated preferences to frame an explicit conversation about how they would work together. At the final session, clients reported on what they had experienced, and the paired results were discussed openly.

Eleven occupational therapists and forty-seven clients aged 23 to 97 took part, recruited from settings ranging from acute hospitals and municipal rehabilitation to psychiatry, home care and specialized neurorehabilitation. Most clients had been referred for neurological conditions. The therapists were all women and none had prior familiarity with the Intentional Relationship Model, making them genuine newcomers to the framework. Recruitment, originally targeting thirty therapists, was hampered by the COVID-19 pandemic, a constraint the authors acknowledge as a major limitation that renders the study exploratory rather than definitive.

The descriptive results painted a coherent picture. Clients rated all six modes as relevant, with median importance scores ranging from 15 to 20 out of a possible 25. Instructing emerged as the most important mode, while Advocating ranked lowest, a pattern echoed in the post-therapy ratings, where clients perceived frequent use of five modes but only occasional use of Advocating. Therapists’ self-ratings followed the same shape, with Advocating rarely used. At the group level, clients and therapists appeared to agree: no obvious discrepancies surfaced between the median profiles.

But when the researchers moved from group medians to individual-level statistics, a more complicated story emerged. Correlations between clients’ preferred modes before therapy and their perceived mode use afterward were moderate to weak, with Spearman’s rho values between 0.26 and 0.48. More strikingly, agreement between clients’ perceptions of mode use and therapists’ self-reported use was weak to trivial, and some correlations were negative, meaning that when a client perceived a mode being used more often, the therapist sometimes reported using it less. In other words, therapists and clients sitting across from each other for weeks of therapy frequently disagreed about the very communication style that defined their collaboration. The authors interpret this cautiously, noting that preferences and perceptions come from different perspectives and may converge differently, but they emphasize that such single-case discrepancies underscore why continuous dialogue about communication is essential rather than optional.

On the utility surveys, clients were largely enthusiastic. Fifty-seven percent felt it was highly relevant to share their communication preferences before therapy, 44 percent found it highly relevant to report and discuss experiences afterward, and 51 percent said discussing preferences significantly shaped the therapy process. Sixty percent judged the time spent on these discussions to be well justified by the benefits, 61 percent found the questionnaire instructions highly sufficient, and 86 percent reported no challenges discussing their preferences with their therapist. Acceptability exceeded 80 percent across both groups.

The therapists were harder to win over. Only 14 percent considered knowledge of client preferences highly relevant to specific therapy processes, and not a single therapist rated information about client experiences as highly relevant for future collaborations. Nearly half felt the time clients spent completing two full questionnaires was not matched by the insight gained, although 86 percent considered their own completion of the therapist form at least somewhat worthwhile. Free-text comments revealed practical friction: some clients, particularly those with cognitive challenges, were overwhelmed by the number of questions or misunderstood the items, requiring substantial assistance. Still, the therapists broadly endorsed the instruments’ acceptability, with one noting that the process had given her direct insight into what worked in individual patient courses, what to attend to, and what to do more or less of in her own therapeutic style.

The study’s significance lies less in any single number than in what it demonstrates about measuring something as elusive as interpersonal style. Structured questionnaires can open a conversation that might otherwise never happen, giving clients a legitimate voice in how they are treated and giving therapists a mirror for their own habits. The Danish versions of the Clinical Assessment of Modes questionnaires appear fit for that purpose, particularly the pre-therapy form, which seems well suited to framing the initial collaboration. At the same time, the authors caution that knowing a client’s preferences will not automatically change the relationship; therapists need deeper training in the six modes and in deliberately shifting between them to avoid suboptimal patterns such as mixing modes or missing a client’s needs entirely. With a small, pandemic-constrained sample and no prior IRM knowledge among participating therapists, the findings demand confirmation in larger, more representative studies, ideally supplemented by qualitative interviews. But the core message stands: clients and therapists do not always perceive the same therapy, and giving both sides a structured way to compare notes may be one of the most practical steps toward genuinely client-centred care.

Subject of Research: Clinical utility of Danish versions of the Clinical Assessment of Modes questionnaires in occupational therapy practice

Article Title: Clinical utility of the Danish versions of the Clinical Assessment of Modes Questionnaires

Article References: Nielsen, K. T., Pilegaard, M. S., Larsen, A. E., & Wæhrens, E. E. (2025). Clinical utility of the Danish versions of the Clinical Assessment of Modes Questionnaires. Scandinavian Journal of Occupational Therapy, 32(1), 1-12. https://doi.org/10.1080/11038128.2026.2615565

Image Credits: AI Generated

DOI: 10.1080/11038128.2026.2615565

Keywords: occupational therapy, therapeutic relationship, Intentional Relationship Model, therapeutic modes, clinical utility, questionnaires, client-therapist discrepancy, communication, psychometrics, Denmark, client-centred practice, rehabilitation

The INSIGHT study, whose name spells out its mission as Identify Nonresponse and Strategies to Improve Questionnaires’ Trustworthiness, adopted a mixed-methods design that treated nonresponse not as a statistical nuisance but as a phenomenon with identifiable, and potentially correctable, causes. The first phase was a needs assessment with two complementary strands. On the quantitative side, the team carried out a statistical analysis of the FASTRACS trial data to pinpoint the subgroups of participants at higher risk of falling silent, comparing the characteristics of respondents and nonrespondents at successive follow-up points. On the qualitative side, they conducted individual interviews with survivors to hear, in their own words, the barriers that had pushed them away from questionnaires and the facilitators that might draw them back.

What emerged from that analysis was strikingly consistent with what survey methodologists have long suspected but rarely tested inside an ongoing oncology trial. Nonrespondents were younger than their responding counterparts, and they reported more systemic side effects from their breast cancer treatment, the fatigue, pain and cognitive fog that follow chemotherapy and endocrine therapy. Three broad domains of influence were identified: the breast cancer experience itself, the design of the questionnaires, and the context in which completion took place, whether at the hospital or at home. A younger woman juggling a return to work, childcare and lingering treatment toxicity, the findings suggest, experiences a mailed or online questionnaire very differently from an older, retired participant with fewer competing demands.

Armed with that diagnosis, the researchers turned to a participatory development process. A brainstorming session brought together breast cancer survivors drawn from the FASTRACS Intersectoral Strategic Participatory Committee, a standing body created to embed patient and stakeholder voices in the project, and additional phone interviews gathered participant perspectives. This was not a token consultation exercise. The ideas that survived into the final intervention were those the survivors themselves judged acceptable, practical and likely to matter to their peers. The researchers then telephoned trial participants to introduce the strategies and to gauge their willingness to complete questionnaires under the new conditions, effectively testing acceptability in real time before formal evaluation.

Four concrete components made it into the FASTRACS-INSIGHT intervention, and their simplicity is part of their charm. Participants received personalized reminder cards, each accompanied by a tea bag, a small gesture of warmth designed to make the request feel human rather than bureaucratic. Printed paper questionnaires were sent to every participant, removing the digital barrier for those who preferred or needed pen and paper. A medical appointments booklet, bundled with a FASTRACS-branded pen, offered a practical tool that kept the study visible in daily life. Finally, the team reinforced telephone-based questionnaire completion, allowing women to answer surveys with a researcher over the phone, and deployed text message reminders timed to prompt action without overwhelming it.

The evaluation phase compared response rates before and after the intervention was rolled out, and the results were unambiguous. At the third follow-up time point, designated T3, questionnaire response rates rose from 54.4 percent to 74.4 percent, a difference that was statistically significant at p less than 0.001. At the fourth time point, T4, the improvement was even more dramatic: completion climbed from 46.2 percent to 76.7 percent, again with p less than 0.001. In plain terms, roughly half of the survivors who had been dropping out of the study’s data collection were brought back into the fold. For a longitudinal randomized controlled trial, in which every lost questionnaire erodes statistical power and invites attrition bias, a gain of twenty to thirty percentage points at successive waves is not a cosmetic improvement but a meaningful rescue of the study’s evidentiary foundation.

The findings also speak to a deeper methodological problem in health research. Survey nonresponse is rarely random, and the INSIGHT study confirms that in the context of cancer survivorship it is patterned by age and by treatment burden. Younger survivors face what the literature describes as distinct work-related, clinical and psychological pressures during the return-to-work period, and women experiencing more systemic side effects may find a lengthy questionnaire an intolerable additional demand rather than a welcome chance to contribute. If researchers ignore these patterns, their longitudinal datasets become progressively skewed toward older, healthier, more engaged participants, quietly distorting the very outcomes, quality of life, fatigue, functioning and employment, that survivorship research aims to measure. By identifying who was at risk and why, INSIGHT offered a template for protecting data quality prospectively rather than statistically patching it after the fact.

Equally instructive is the participatory character of the solution. None of the four components, the reminder cards with tea bags, the universal printed questionnaires, the appointments booklet with pen, or reinforced phone completion and text reminders, involved sophisticated technology or prohibitive cost. What distinguished them was that they were co-designed with the people expected to use them. The authors argue that involving breast cancer survivors in developing the intervention was essential to tailoring it to their needs, and the trial’s before-and-after results give empirical weight to that claim. The approach echoes the intervention mapping framework the FASTRACS team has used throughout the parent trial, in which theory, evidence and stakeholder input are systematically combined to design health behavior interventions. Response behavior, INSIGHT demonstrates, is itself a behavior that can be mapped, understood and changed.

The implications extend well beyond this single French trial. Randomized controlled trials in oncology routinely lose participants to questionnaire fatigue, and systematic reviews of survey methodology have catalogued dozens of strategies, from monetary incentives to shortened instruments, with mixed and context-dependent results. What INSIGHT adds is a replicable, multi-step playbook: analyze who is not responding, interview a sample to understand their reasons, co-develop low-burden interventions with a patient committee, and evaluate the package with straightforward before-and-after comparisons. The registered protocols underlying the work, the FASTRACS-RCT under NCT04846972 and the RECOVA-FASTRACS qualitative protocol under NCT05919498, with an amendment covering INSIGHT, give the effort a transparency that methodologists will welcome. Informed consent was obtained from all participants, and the authors report no competing interests, with funding from the League against cancer in the Auvergne Rhône-Alpes region, the National Agency for the Improvement of Working Conditions, the National Cancer Institute, and joint support from the Pension and Welfare Fund for Notaries’ Clerks and Employees.

There is a quietly radical message here for the research enterprise. Behind every unreturned questionnaire is a person navigating one of the hardest chapters of her life, and the difference between a study that retains its participants and one that loses them may come down to whether the researchers asked those participants what would help. A tea bag and a handwritten-feeling reminder card will not cure cancer or eliminate treatment side effects, but they signal respect, and respect, as the FASTRACS-INSIGHT team has shown with response rates rising from the mid-forties to the mid-seventies, turns out to be a remarkably effective instrument of science. For the growing community of survivorship researchers studying return to work after cancer, an outcome with well-documented personal and economic stakes, the lesson is clear: sustainable data collection is not a logistics problem to be solved in a back office, but a relationship to be built with the survivors themselves, one thoughtfully designed touchpoint at a time.

Subject of Research: Nonresponse causes and response-rate improvement strategies in a randomized controlled trial of return-to-work support for breast cancer survivors

Article Title: Understanding nonresponse and strategies to improve response rates in a randomized controlled trial among breast cancer survivors: the FASTRACS-INSIGHT mixed-methods study

Article References: Soulier, A., Blazer, A., Préau, M., Guittard, L., Maurin, A., Fervers, B., Letrilliart, L., Carretier, J., Broc, G., Fassier, J.-B., Péron, J., Rouat, S., & Lamort-Bouché, M. (2026). Understanding nonresponse and strategies to improve response rates in a randomized controlled trial among breast cancer survivors: the FASTRACS-INSIGHT mixed-methods study. Journal of Cancer Survivorship. https://doi.org/10.1007/s11764-026-02132-z

Image Credits: AI Generated

DOI: 10.1007/s11764-026-02132-z

Keywords: breast cancer, cancer survivors, randomized controlled trial, survey nonresponse, questionnaires, response rates, return to work, cancer survivorship, mixed-methods study, participatory research, reminder strategies, data quality