Only about four in ten adolescents receiving care for depression while away from school say they intend to re-enroll, according to a cross-sectional survey of 331 patients conducted at two hospitals in Hunan, China. The study, published in Child and Adolescent Psychiatry and Mental Health, also introduces a preliminary prediction tool built to estimate an individual patient’s likelihood of wanting to return, based on factors that clinicians and families could potentially observe during routine care.
The participants were all either on formal academic leave or had been away from school for more than three months when they were surveyed between April and December 2023. Recruitment took place in outpatient departments and inpatient wards, and the researchers describe the sample as a convenience sample, meaning patients were enrolled as they presented at the participating services rather than through random population sampling. Depression in adolescence is common enough that academic leave has become a visible consequence, yet the authors note that these individuals may face real difficulties when the time comes to go back.
To build the model, the team randomly divided the 331 participants into a training set containing 70 percent of the sample and an internal validation set containing the remaining 30 percent. The questionnaires covered demographics, experiences related to re-enrollment, a self-designed instrument measuring re-enrollment intentions, depression severity, academic self-efficacy, study stress, family function, social support, and perceived discrimination. Binary logistic regression was then used to identify which of these variables independently predicted the intention to re-enroll, and the surviving predictors were assembled into a nomogram, a graphical scoring device that lets a user total up points assigned to each factor and read off an estimated probability.
The headline result is stark: among adolescents on leave, only 40.5 percent intended to re-enroll. Five variables emerged as key predictors of that intention: academic self-efficacy, perceived school support, depressive symptoms, social support, and grade level. In the internal validation cohort, the nomogram showed promising discriminative ability, meaning it separated students who intended to return from those who did not, and acceptable calibration, meaning its predicted probabilities tracked the observed rates reasonably well. Decision curve analysis, a method for judging whether a model would improve decisions across a range of possible clinical thresholds, suggested potential clinical utility.
The authors are careful about the limits of what they built. The model was internally validated only, on a single-center dataset drawn from two hospitals in one Chinese province, and they describe it as having preliminary potential that may offer a basis for future research on risk assessment rather than a finished clinical instrument. External validation in different settings, and ideally a prospective design, would be needed before such a tool could guide individual decisions. The cross-sectional design also means the survey captured intentions at one moment in time, not whether students actually returned to school afterward.
Even so, the pattern of predictors carries practical weight. Academic self-efficacy, a student’s belief in their own capacity to handle schoolwork, and perceived school support had the most significant impact on re-enrollment intentions in this sample. The conclusion frames the influencing factors as multifaceted and suggests that multidisciplinary efforts to enhance self-efficacy and school support merit further investigation as potential strategies to foster the desire to return. That framing stops short of claiming effectiveness; it identifies candidate targets for future intervention research rather than proven remedies.
For clinicians, the nomogram approach offers a way of translating a set of routine assessments into an individualized estimate. Rather than treating return-to-school readiness as a single clinical question, the model aggregates information across psychological, academic, and social domains. Depression severity appears among the predictors, but so do factors that lie partly outside the clinic: how supported a student feels at school, how confident they feel academically, and how supported they feel more generally. Grade level, too, matters, which fits the intuition that the stakes and structures of returning differ between a younger adolescent and a student approaching the end of secondary school.
The study’s setting deserves emphasis. All participants were adolescent depression patients on leave or long absent from school in Hunan, China, surveyed in hospital-based services. School systems, leave policies, and pathways back to education differ considerably across countries, so the specific predictors and their weights may not transfer directly elsewhere. The 40.5 percent figure describes this particular sample, not a global rate of return intention among depressed adolescents, and the convenience sampling further limits how representative the numbers are.
What the study does establish is a documented, quantified gap: a majority of these adolescents, nearly six in ten, did not intend to re-enroll at the time of the survey. By attaching that gap to a small set of measurable predictors and packaging them in a validated-in-principle scoring tool, the researchers have given subsequent work a concrete starting point. Whether boosting a student’s academic self-efficacy or strengthening perceived school support actually raises re-enrollment rates is a question the authors explicitly leave open, one that will require intervention studies rather than another survey.
Until such trials exist, the practical takeaway is observational. Adolescents on academic leave for depression are a group in which return intentions are far from universal, and the factors that travel with those intentions span the classroom, the family, the peer and support network, and the symptoms themselves. A tool that flags low likelihood of intending to return could, if future validation supports it, help schools and mental health services direct limited support resources toward the students who need the most help imagining a way back.
Subject of Research: Development and internal validation of a nomogram predicting re-enrollment intentions among adolescent depression patients on academic leave in China
Article Title: Development and validation of a risk prediction nomogram model for re-enrollment intentions in adolescent depression patients on academic leave: a cross-sectional study
Article References: Tian, D., Liu, C., Xie, S., & Yang, M. (2026). Development and validation of a risk prediction nomogram model for re-enrollment intentions in adolescent depression patients on academic leave: a cross-sectional study. Child and Adolescent Psychiatry and Mental Health. https://doi.org/10.1186/s13034-026-01163-9
Image Credits: AI Generated
DOI: 10.1186/s13034-026-01163-9
Keywords: adolescent depression, academic leave, re-enrollment intentions, nomogram, prediction model, academic self-efficacy, school support, social support, cross-sectional study, logistic regression, decision curve analysis, China
News Source: Glenn Wilkins. (October 30, 2026). Nomogram flags which depressed teens on school leave plan to return to class. Scienmag.
