Matching needs to services: Development of a service needs index for determining care pathways in youth mental health
Bibliographic record
Abstract
OBJECTIVE: To develop a Service Needs Index that measures a young person's needs across domains relevant to care provision and to examine the index's construction under different assumptions. METHODS: = 2193) aged 12-25 years who sought help at youth mental health services across Australia were invited to use a digital platform (Innowell) as part of their care and complete a multidimensional assessment. Using online assessment data from the eligible 1611 individuals (73.5%), a Service Needs Index comprising three sub-indices (Clinical, Psychosocial, and Comorbidity) was constructed under two weighting approaches, an equal weighted scheme and a weighting scheme constructed with expert input and correlation-optimisation. These approaches were examined and compared. RESULTS: The Clinical, Psychosocial, and Comorbidity Indices were derived using standardised questionnaires to assess mental health symptoms and history, work and social functioning, and physical health and substance use, respectively. The expert input weighting scheme was favoured with less output uncertainty. Among those with the top 25% of Clinical Index scores, almost half also belonged in the group with the top 25% of Psychosocial Index scores, while 11.9% of the total sample were in the bottom 25% percentiles for both Clinical and Psychosocial Index scores. CONCLUSION: These indices should be assessed in real-world settings before recommendations are made about their feasibility and acceptability; however, the indices could differentiate between needs to guide individual-level decision-making about service pathways for young people. Furthermore, population-level analyses of these aggregated indices can inform strategic decisions related to service planning and design.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| 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".