A ‘navigator’ model in emerging mental illness?
Bibliographic record
Abstract
AIM: While there is clearly much to be gained from ensuring that youth with emerging mental illness across a variety of psychiatric illnesses receive care that reduces symptoms and improves functioning, it is not at all clear how best to achieve these results within a health-care system that has limited resources. Outside of the area of psychosis, there is little evidence to guide us around a model of care that might be effective, efficient and linked to existing mental health systems. METHODS: We summarize the literature on early intervention (EI) in psychosis and derive five key lessons for transdiagnostic prevention. We then broadened our search to find clinical and systems models that shared challenges similar to those identified for EI, high levels of patient and family distress, need for rapid yet comprehensive diagnostic assessment and timely initiation of specific treatment. RESULTS: Cancer navigators have numerous functions that appear to overlap with the key issues in transdiagnostic psychiatric EI. A navigation clinic with a separate identity, but clearly connected to specialized mental health facilities has the potential to speed assessment, diagnosis and treatment streaming. Navigators would be involved with youth and their family throughout different levels of care, making clinical decisions based on illness and functional status. CONCLUSIONS: In sum, the evidence from navigation services in cancer care offers the mental health field a progressive clinical model that might be an important guide for EI in youth.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.012 |
| Scholarly communication | 0.004 | 0.011 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".