The participants’ mental health data used to develop and validate the LBA model were drawn from a previously conducted longitudinal study focusing on assessing depression, anxiety, stress, suicidality, and self-harm [45]. Participants completed a comprehensive assessment survey at the beginning and end of the current study’s period, as well as shorter biweekly surveys in between. The surveys assessed mental and physical health through 3 response formats: brief written paragraphs (text responses), writing or selecting words from a list (word responses), and standardized rating scales. The order of the response formats (ie, open-ended questions or rating scales first) was randomized. The median estimated response time for the 4 language responses included in the final model (see the Model Development section) was approximately 4.79 (mean 6.14, SD 7.84) minutes.

In accordance with the SEMP framework, the first preregistration [52] outlines the model development, student assessment procedure, sample size, and exclusion criteria. After model development, a second preregistration [53] specified the final models and refined hypotheses tested in a held-out set. The final models are available in the LBA model library [54] and can be explored at our public demo page [55].

Participants from the United States and United Kingdom were recruited via Prolific [56] and followed online for 10 weeks between June and December 2021. The participant sample was enriched, with approximately half of the participants self-reporting having been diagnosed with a mental health diagnosis of major depressive disorder (MDD [57]; ≈25%) or Generalized Anxiety Disorder (GAD [57]; ≈25%). Of the total sample, 212 participants were assigned to a held-out validation set based on having received assessments from multiple psychotherapists, and 600 were assigned to the development set (see the Selection of Development Sample section).

The sample was predominantly female (development: 396/600=66%; validation: 114/212=53.8%) with a mean age of 43.9 (SD 18.2) years in the development set and 40.2 (SD 13.0) years in the validation set. Participants in the validation set were, on average, slightly younger, more likely to hold a university degree, more likely employed, and more often male compared to the development set. Full sociodemographic details are presented in Figure 1 and Multimedia Appendix 1.

This study received ethical approval from the Swedish National Ethics Committee (Dnr 2021‐01820). Participants were informed about this study and that participation was voluntary. Consent was obtained before starting this study. Where participants answered open-ended questions on suicide and self-harm, they were required to tick a box indicating that they understood that the survey was anonymous and that no one would be able to contact them in relation to their responses, and they were provided information about where they could retrieve help (eg, suicide prevention helplines). They were compensated through money (£7.5/h; a currency exchange rate of GB £1=US $1.37 was applicable) on Prolific. The data collection of student assessments was approved by the Ethics Committee of the University of Mainz (2024-JGU-psychEK-022). Student assessors were compensated through course credit.

Criterion validity (H1) was evaluated using the Pearson correlation between the LBAs and the best-estimate assessments in the validation set (n=212). A correlation of r≥0.70 was set as the threshold for adequate criterion validity [75]. We additionally computed a 1-sided disattenuated correlation [76]. Disattenuation estimates what the criterion validity correlation would be if the reference standard were measured without error—that is, if the psychotherapists agreed perfectly on the need for mental health support. The correction is 1-sided because only the reference standard, not the LBA, is corrected for unreliability. We used the interrater reliability among the psychotherapists (Krippendorff α) as the correction factor. As error-free measurement is unattainable in practice, the disattenuated correlation should be interpreted as an upper-bound estimate rather than a point estimate.

Convergent validity (H2) was evaluated using Pearson correlations between the LBAs and rating scales in the validation set (n=212). We expected significant, at least medium-sized, correlations of |r|>=0.30 [77]; specifically, positive correlations with depression (PHQ-9; H2a), anxiety (GAD-7; H2b), stress (PSS-10; H2c), and suicidality (IDAS; H2d); and negative correlations with life satisfaction (SWLS-3; H2e) and harmony in life (HILS-3; H2f). External validity (H3) was evaluated using Pearson correlations between the LBAs and the behavioral measures. We expected significant, at least small (r≥0.10) [77], positive correlations with the number of sick days (H3a) and the number of health care visits (H3b).

We also explored a parsimonious model using only the mental health text as a predictor and evaluated its criterion, convergent, and external validity (H1-H3). Such a model offers practical advantages, as open questions about mental health are commonly included in studies, interviews, and existing datasets, and requires less response time. The significance of all validity correlations (H1-H3) was 2-sided tested. Despite the directional hypotheses, we retained 2-sided tests as the more conservative choice.

Finally, face validity (H4) was assessed by examining how linguistic topics correlate with the LBAs. Unlike H1-H3, which evaluated model performance on held-out data, this analysis aimed to provide insights into the model’s decisions through language patterns. Topics were extracted by applying latent Dirichlet allocation (LDA [78]; see the Language-Based Visualizations section) on the complete dataset (n=812). LDA is an unsupervised method that is fully independent of the word embeddings, the ridge regression, and the human assessments. This justifies using the full sample, in addition to the fact that a larger sample yields more stable and interpretable topics. Yet, this analysis should be interpreted as descriptive face validity evidence rather than a test on unseen data. An a priori power analysis determined that with 812 participants, α=.05, and 90% power, correlations of |r|≥0.113 would be statistically significant [79]. As topic-outcome correlations typically remain below |r|=0.30, we expected at least 4 significant topics per text variable and 2 significant topics per word variable.

We examined linguistic patterns associated with the LBAs using 2 complementary methods [80]: LDA [78] and supervised embedding projections [72]. LDA is a topic modeling method that identifies latent topics within a text corpus based on word frequencies and co-occurrences. We extracted 12 topics per text variable and 2 topics per word variable. Before topic extraction, stop words [81], repeated phrases from the questions, and highly frequent terms were removed, with frequency thresholds set individually per variable (ranging from 150 to 450 occurrences). Co-occurring words were structured into a Document Term Matrix, allowing for 1- to 3-grams. Topic distributions within each response were then used to predict the LBAs via linear regression, with standardized regression weights serving as effect sizes. False Discovery Rate corrections [82] were applied within each language variable.

Supervised embedding projections [72] provided a second, complementary visualization based on word embeddings rather than word frequency. This method identifies words significantly associated with the LBAs by comparing responses from the highest and lowest quartiles of the assessed need for support. Embeddings from both groups were aggregated, and a difference vector was computed. Each word was projected onto this vector using the dot product, mapping its association with the LBAs. Statistical significance was assessed against a permuted null distribution, with False Discovery Rate corrections [82] applied within each variable. Stop words [81] were removed before analysis.

All analyses were performed in R (version 4.4.1) [83]. The model development and supervised embedding projections were performed using the text package (version 1.2.3) [72]. LDA topics were extracted with the topics package (version 0.40.1) [84]. Data wrangling, descriptive analyses, and visualizations were carried out with the packages dplyr (version 1.1.4) [85] and ggplot (version 3.5.1) [86]. Krippendorff α was calculated using the irr package (version 0.84.1) [87].

Participants wrote between a median of 9 with IQR 5-15 (self-harm) and 44 with IQR 30-68 words (mental health) in the text response variables (Table S4 in Multimedia Appendix 1). A substantial proportion of participants (development: 240/600 or 40%; validation: 94/212 or 44.3%) fell within clinically relevant ranges of at least moderate depression and/or anxiety according to PHQ-9 and GAD-7 thresholds [58,59]. All rating scales demonstrated good to excellent internal consistency in both the development and validation sets (Cronbach α=0.85-0.96; see Table S5 in Multimedia Appendix 1 for scale-specific values).

On average, participants’ need for mental health support in the validation set was 2.55 (SD 1.07) as assessed by the students and 2.50 (SD 1.16) as assessed by the psychotherapists; this difference was not significant, t₂₁₁=1.064, P=.29. An exploratory analysis of where the student and best-estimate assessments converged and diverged is reported in Multimedia Appendix 1, as well as full descriptive statistics of the language responses, rating scales, and need for mental health support assessments (Tables S4 to S7 in Multimedia Appendix 1).

Comparing embedding optimization methods revealed that prepending questions to responses before embedding extraction significantly improved model performance (see Table S8 in Multimedia Appendix 1). This finding is theoretically plausible because contextual embeddings represent each token conditional on its surrounding text. Short responses in particular (especially in the suicidality text responses, eg, “no, never”) are often ambiguous in isolation, and prepending the corresponding question anchors the response to the construct being probed (eg, denial of suicidal ideation). Accordingly, this approach was adopted in all subsequent analyses.

Using only the mental health text response, the model achieved a training accuracy of r=0.79 (Table 2). In the first step of the step-forward procedure, adding suicidality yielded the largest increase in accuracy (r=0.79 to r=0.84). In the second step, medical history further improved accuracy (r=0.86). In the third step, only depression words significantly improved the model. Despite a small increase (Δr=0.01), we included it because participants typically select these words in under a minute. The final 4-variable model achieved a training accuracy of r=0.87.

The final model achieved a correlation of r=0.82 with the best-estimate assessments, 95% CI 0.77-0.86 in the held-out set, exceeding the r=0.70 threshold for criterion validity (H1; Table 3). Notably, the 2 individual psychotherapists agreed with each other at r=0.78, meaning the model’s alignment with the best-estimate (r=0.82) already exceeds the level of agreement between individual clinicians. Correcting for imperfect interrater reliability among the psychotherapists increased the correlation to nearly perfect (r_corrected=0.96 with Krippendorff α_{psychotherapists} =0.73). This upper-bound estimate indicates that the observed r=0.82 is close to the maximum achievable given the reliability of the reference standard. Further, the correlation between the LBAs and student assessments was the same in the validation set as in the development set (r=0.87), indicating excellent generalization. The LBAs correlated more strongly with student assessments (r=0.87) than the best-estimate assessments did (r=0.82), likely because both the model and the students based their assessments on the same language responses.

The LBAs further demonstrated strong convergent validity (H2; Table 4), including strong positive correlations with depression (r_PHQ=0.77), anxiety (r_GAD=0.72), stress (r_PSS=0.76), and suicidality scores (r_IDAS=0.62), as well as strong negative correlations with life satisfaction (r_SWLS=−0.66) and harmony in life (r_HILS=−0.64). All correlations were statistically significant (P<.001) and classified as large effects [77]. What is important to note is that the criterion and convergent validity estimates are not completely independent signals due to shared method variance: the rating scales were part of the data evaluated by the psychotherapists to achieve the best-estimate assessments, an inherent property of LEAD designs (ie, all data [46]; see the Discussion section). Regarding external validity (H3), the LBAs correlated significantly with the number of health care visits (r=0.16, P<.05), meeting the threshold for a small effect size [77], but not with the number of sick days (r=0.09, not significant). Distributions of the LBAs and human assessments, as well as analyses of model residuals, are reported in Figures S2-S5 in Multimedia Appendix 1.

The parsimonious model using only mental health texts demonstrated strong performance, only modestly lower than the full model. The LBAs showed a correlation of r=0.79 with student assessments during model development (Table S10 in Multimedia Appendix 1), and a correlation of r=0.77 with the best-estimate assessments in the validation set (Table 3). It showed significant large correlations with the PHQ-9, GAD-7, PSS-10, SWLS-3, and HILS-3, but a comparatively small correlation with IDAS suicidality scores (Table 4 and Table S11 in Multimedia Appendix 1). Correlations with the behavioral measures were in the expected direction, but did not reach significance.

In an exploratory post hoc analysis, we compared the LBAs against demographic, dictionary- and frequency-based baseline models trained and validated in the same pipeline. The LBAs outperformed all baselines, with the largest advantage for the parsimonious model in comparison to the language baseline models (see Figure S6 and Table S12 in Multimedia Appendix 1). In a further exploratory analysis, the baseline LBAs predicted depression, anxiety, stress, and suicidality scores 10 weeks later, beyond the respective baseline scale score (Table S13 in Multimedia Appendix 1)

Finally, the extracted LDA topics derived from the complete dataset (n=812) were well-interpretable for all 4 language responses (see Figure 4). We extracted 12 topics per text response variable and 2 topics per word response variable (see the Methods section). Eleven topics for mental health, 11 topics for suicidality, and 7 topics for medical history correlated significantly with the comprehensive LBA model. The significant topics ranged from |β|=0.08 to |β|=0.36. For the depression words, both topics were significantly associated with the LBAs, showing large associations of |β|=0.76. Overall, the number of significant topics exceeded the predefined threshold and aligned with established psychological theories [49-51], supporting face validity (H4).

Topics significantly associated with LBAs of low need for support included “work,” “home,” “happy,” “fine,” the absence of suicidal thoughts, good health, and rarely getting sick. Prominent words in the associated depression words topic included “hopeful,” “content,” and “optimistic.” These topics align with the broaden-and-build theory [49] that suggests that positive emotions expand and facilitate cognitive and behavioral patterns, which increases well-being and social connectedness. Surprisingly, the mental health topics referring to anxiety, problems in life, and the COVID-19 restrictions were also significantly associated with lower need for mental health support, despite not being inherently positive.

Topics significantly associated with LBAs of high need for support referred to anxiety, depression, the perception of others (“people”); comparisons (“like”); thinking about, planning, or attempting suicide; and thinking that others would be better off without them. Within the medical history variable, both acute mental health issues (eg, taking psychiatric medication) and long-term mental health issues (indicated by words such as “since,” “age,” “time,” and “suffered”) were significantly related to higher LBAs. Prominent words in the associated depression words topic included “anxiety,” “lethargic,” and “pessimistic.” These topics correspond to theories of depression [50,51] that suggest that negative cognitive patterns and attentional biases lead to negative affect, which can lead to or reinforce symptoms of depression and anxiety.

The exploratory supervised embedding projections (in contrast to the LDA topics, only based on the held-out set; Figure 5) revealed many words significantly associated with LBAs of low or high need for mental health support. Overall, the direction and significance of words matched those found in the LDA topics (Figure 4). For instance, words such as “happy,” “good,” and “hopeful” were significantly associated with low need for support, while “anxiety,” “depression,” and “overwhelmed” were associated with high need. In the suicidality variable, words explicitly denying “suicidal” “thoughts” (eg, “not,” “never,” and “no”) were strongly associated with low need, whereas words describing suicidal intent (eg, “plans,” “attempting,” and “committing”) were associated with high need. Notably, the word “blood” in the projection plot of the medical history variable likely refers to high blood pressure, as inferred from the LDA topics. This example shows how LDA topics and 3-grams can provide slightly more contextual grouping, while supervised embedding projections more directly reflect the LBA model’s underlying process. Overall, these results further support the face validity of the LBAs by corroborating the LDA topic findings using a different method [80].

This study developed and validated LBAs of need for mental health support using natural language responses. We validated 2 models: a parsimonious model using only a mental health text response, and a comprehensive model that additionally includes texts about suicidal thoughts and medical history, as well as depression-related words. Responding to all 4 language variables took participants less than 5 minutes in total.

Using the SEMP framework, models and hypotheses were preregistered before testing on a held-out validation set. The LBAs in the held-out set demonstrated strong criterion validity, correlating strongly with the best-estimate assessments of experienced psychotherapists who had access to longitudinal patient reports. The LBAs further showed strong convergent validity, correlating strongly with established measures of depression, anxiety, stress, and suicidality, and strongly negatively with established measures of well-being. Significant associations with linguistic topics were consistent with established psychological theories: low need for support assessments were associated with positive emotions, relationships, good health, and lack of suicidal thoughts, while high need for support assessments were linked to depression, anxiety, suicidal thoughts, and an ongoing history of mental illness. These associations were confirmed through 2 independent methods, LDA topic modeling and supervised embedding projections.

Much progress has been made in LLMs’ contextual language understanding and predictive accuracy [16,28]. However, for LLM-driven mental health assessments, the absence of a ground truth makes model training particularly complex. Best-estimate assessments are crucial for establishing a valid reference standard against which models can be evaluated, and averaging the assessments of multiple psychotherapists based on longitudinal clinical data represents one of the strongest available approaches [47,88]. It is important to note that the longitudinal clinical data evaluated by the psychotherapists also included the rating scales (ie, all data in the LEAD design [46]), which makes the benchmarks for criterion and convergent validity not completely independent due to shared method variance.

The observed interrater reliability among the individual psychotherapists (Krippendorff α=0.73) highlights that even experienced experts sometimes disagree on mental health assessments. Given that this reliability sets a theoretical upper limit on expected validity [30], it is notable that the LBAs achieved a correlation of r=0.82 with the clinicians’ best-estimate assessments. This suggests that the model captures a more stable signal than a single clinician, likely because it applies the same weighting of linguistic features consistently across cases, whereas individual clinicians may vary in what information they prioritize. However, the model lacks the clinical flexibility to recognize atypical presentations or contextual factors not captured in the text, which remains an important advantage of human judgment. This underscores the potential for a complementary approach, where the model provides a consistent baseline assessment that clinicians can then refine based on their expertise.

Remarkably, the assessments of the graduate students—on which the model was trained—converged closely with the best-estimate assessments, despite the students’ more limited clinical experience and their access to only the baseline language responses and demographics. Yet, an exploratory post hoc analysis indicated that the psychotherapists’ divergence from the student assessments was largely systematic rather than random. The difference between student and best-estimate assessments was substantially explained by the clinical rating scales that only the psychotherapists had seen (see Figure S8 and Table S14 in Multimedia Appendix 1 for the full variance-reconstruction analysis). Still, the high overall convergence supports training LBA models on assessments from carefully instructed raters when expert capacity is limited, provided that the validation is conducted against an expert-based reference standard.

While the parsimonious model already demonstrated a high accuracy, adding an explicit question about suicidality significantly improved model performance. The parsimonious model seemed to particularly miss information about suicidality, as evidenced by the correlation with IDAS suicidality substantially increasing from the parsimonious to the comprehensive model. This advantage could also be observed for the criterion validity, yet it is partly attributable to how the criterion itself was constructed: the best-estimate assessments were based on psychotherapists’ evaluations of longitudinal clinical data centering on internalizing disorders, self-harm, and suicidality. Moreover, the psychotherapists assessed participants’ need for mental health support immediately after assessing self-harm and suicidality [45] (see Table S2 in Multimedia Appendix 1), likely inflating the correlation between these assessments. Besides design characteristics, suicidal ideation and especially intention are evidently directly related to someone’s need for mental health support. Suicidal ideation remains a highly stigmatized topic and is often not disclosed spontaneously [89], highlighting the importance of explicitly prompting for suicidality to enhance the sensitivity of LBA models to this critical issue. Crucially, meta-analyses and reviews show that actively asking individuals about suicide does not appear to induce or increase suicidal ideation [90,91], affirming that explicit screening is safe.

To situate the contribution of the embedding-based approach within more traditional language analysis methods, we compared the LBAs with 2 established alternatives trained in the same pipeline: a tf-idf (term frequency-inverse document frequency) bag-of-words model and a model trained on a negation-aware suicide dictionary and LIWC-22 (Linguistic Inquiry and Word Count) dimensions [92]. The LBAs outperformed both baselines in the held-out set, while both language-based baseline models clearly outperformed the demographics-only model (see Figure S6 and Table S12 in Multimedia Appendix 1 for the full analysis). Notably, the advantage of the embeddings was modest when all 4 language variables were available, but substantial for the parsimonious models based only on the single mental health response. This shows that contextual embeddings maintain accuracy with brief responses, the scenario most relevant for large-scale screening.

Beyond these traditional baselines, our approach can be situated relative to generative encoder-decoder architectures increasingly used for LBAs by prompting an LLM to directly output a score from text. Zero-shot LLMs can approach human-rater accuracy but remain prompt-sensitive, performing best combined with document-tuned embedding-and-regression pipelines [93]. We do not argue for one method over the other, but a document-tuned encoder paired with ridge regression is well suited to person-level psychological assessment: its regularization supports stable estimates under sample size constraints, the model weights yield deterministic outputs that remain stable over time, and document-tuned LLM embeddings have been repeatedly benchmarked as strong, efficient representations of psychological constructs from text [94]. Practically, this architecture is also lightweight: with 335 million parameters, the model can be run on standard hardware and entirely on-site. In applied settings such as digital health care providers, sensitive free-text responses can be processed without transmitting them to external providers, at low computational cost.

The LBAs correlated strongly with mental health scales, yet only partly with behavioral measures. The null finding for sick days may reflect the measure’s limited variance in this sample (n=190 or 90% of validation-set participants reported 0 sick days over the past 3 months), rather than an insensitivity of the LBA to functional impairment.

Comparing the LBAs to rating scales requires caution, since the best-estimate assessments were based on both language responses and rating scale scores, making them difficult to use as an independent benchmark. The students based their assessments solely on language responses, so it is unsurprising that the LBAs correlated more strongly with student assessments (r=0.87) than the PHQ-9 did (r=0.75). Conversely, the psychotherapists had access to rating scales alongside language and clinical data, which likely explains why the best-estimate assessments correlated slightly more strongly with the PHQ-9 (r=0.84) than with the LBAs (r=0.82). These patterns reflect what information each assessor had access to, rather than the inherent superiority of either method. However, a recent investigation of the PHQ-9 showed that most respondents misinterpreted its instructions, basing responses on perceived bothersomeness rather than symptom frequency as intended [95]—a limitation potentially shared by similar scales. While this points to the scales being imperfect benchmarks for convergent validity, it reinforces the value of the criterion validity analysis against the best-estimate assessments as the primary validation approach. Beyond accuracy, LBAs offer unique advantages: they capture nuanced, real-life expressions of mental health and avoid the rigidity of rating scales [16,28]. Additionally, clinicians can review the written responses themselves alongside the numerical score, enabling a hybrid approach in which statistical evaluation and clinical judgment complement each other.

The model exhibited a regression-to-the-mean effect by systematically overestimating low need for support cases and underestimating high need for support cases (see Figures S2-S5 in Multimedia Appendix 1). This is common in regularized predictive models since they penalize coefficients to prevent overfitting, which shrinks predictions toward the mean of the criterion [48]. This can be mitigated by distribution-based corrections, which realign the distribution of the predictions with that of the human assessments—either by transforming the criterion before training [48] or by remapping the predictions post hoc. Our demonstration (Multimedia Appendix 1) shows that post hoc transformations substantially reduce the overestimation of low-severity cases and underestimation of high-severity cases—without lowering criterion validity (Figure S9 and Table S15 in Multimedia Appendix 1).

The need for mental health support assessed here is limited to internalizing symptoms and suicidality, and the model was trained and validated on a sample enriched for these conditions. While this coverage addresses the most common clinical needs worldwide—depression and anxiety alone account for approximately 63% of mental health diagnoses [13]—the model’s performance on related (eg, eating disorders and posttraumatic stress disorder) or less related conditions (eg, attention-deficit/hyperactivity disorders and psychotic disorders) is unknown. Additionally, while the student and psychotherapist assessors also had access to participants’ descriptions of their social situation and support system, it is uncertain whether the LBA model captures, for example, social determinants (eg, loneliness) of support need. More broadly, applying the model to populations with different demographics, cultural backgrounds, or base rates of mental health conditions may reduce its accuracy or introduce biases [96]. This concern is consistent with prior work showing differential accuracy across racial or ethnic groups, such as regarding youth depression scores [97] and language markers of depression [98].

Lastly, this study assesses the need for mental health support on a 5-point scale, which has the advantage of being simple, interpretable, and comparable across individuals. Beyond the numerical output, the practical value of the scale lies in how the item formulations map onto a graded set of actions for those assessed. A complementary approach could involve using a generative LLM to produce personalized verbal support recommendations based on the individual’s responses. For example, lower scores could point to appropriate self-directed resources (eg, psychoeducation and lifestyle guidance), while higher scores could surface potentially suitable clinician-led initiatives and available crisis helplines. Embedding this into a digital health system could let the LBA function as a triage layer. The nondiagnostic, support-framed output may also help address attitudinal barriers to help-seeking by lowering reluctance and making screening feel lower-stakes. Future work could compare engagement with “need for support” vs diagnostic framings, and whether this effect is stronger among populations with higher baseline stigma around mental health conditions.

The model could be used in research settings to screen participants’ need for mental health support, for example, to stratify study groups or identify individuals who may benefit from intervention. Its short application time and strong alignment with best-estimate assessments make it practical for large-scale screening where comprehensive clinical evaluation is not feasible.

In real-world settings, many people affected by mental health issues do not recognize a need for treatment, which makes help in early identification, support, and intervention crucial [2,4,7]. LBAs of the need for mental health support could complement existing screening tools and early intervention efforts. Possible applications include digital health platforms, informational websites on mood-related disorders, or health insurance portals, where individuals could receive preliminary guidance based on their responses. As the model assesses the need for support rather than psychiatric diagnoses, it may also help to reduce stigma surrounding mental health issues [33], encouraging more people to seek support. Implementing such a screening system in a real-world setting would require rigorous calibration to ensure fair, unbiased, and ethical prioritization. This is especially important when the target group differs in demographics, cultural background, or mental health condition base rates from the current enriched sample.

This study demonstrates that an LBA model based on natural language responses can closely align with experienced psychotherapists’ assessments of need for mental health support, using less than 5 minutes of respondent time. The model demonstrated strong criterion, convergent, and face validity against best-estimate reference assessments based on longitudinal data. By assessing the overall need for support based on individuals’ own descriptions of their mental health, rather than predicting single diagnostic labels, this approach may offer a less stigmatizing and potentially a more flexible alternative suited for early-stage mental health screening. Both the comprehensive and parsimonious models are openly available at the LBA model library [54] to support further and independent validation.