A multidimensional approach for differentiating the clinical needs of young people presenting for primary mental health care
Bibliographic record
Abstract
OBJECTIVES: There is an ongoing necessity to match clinical interventions with the multidimensional needs of young people. A key step toward better service planning and the design of optimal models of care is to use multidimensional assessment to understand the clinical needs of those presenting to primary mental health care. METHODS: 1284 people aged 12-25 years presenting to primary youth mental health services completed an online assessment at service entry. Latent class analysis was conducted for seven scales assessing anxiety, depression, psychosis, mania, functioning (indexed by Work and Social Adjustment Scale), and suicidality. RESULTS: A three-class solution was identified as the optimal solution. Class 1 (n = 305, 23.75%), an early illness stage group, had low and mixed symptomatology with limited functional impairment, class 2 (n = 353, 27.49%) was made up of older persons with established depression and functional impairment, and class 3 (n = 626, 48.75%) had very high and complex needs, with functional impairment, suicidality, and at-risk mental states (psychosis or mania). Additional differentiating characteristics included psychological distress, circadian disturbances, social support, mental health history, eating disorder behaviours, and symptoms of post-traumatic stress disorder. CONCLUSIONS: A large proportion of help-seeking young people present with symptoms and functional impairment that may exceed the levels of care available from basic primary care or brief intervention services. These subgroups highlight the importance of multidimensional assessments to determine appropriate service pathways and care options.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".