Early intervention for psychiatry itself: the invisible hands for future psychiatry
Bibliographic record
Abstract
This narrative review probes the future trends of psychiatry from the perspectives of professionals working in the field of early intervention for psychosis and youth mental health. The review is co-constructed by a diverse group of clinicians and researchers, including those with lived experience, working in high- to low-resource settings in the Asia-Pacific. Grounded in the consideration of psychiatry as a medical discipline. When the early intervention lens is applied to the state of psychiatry itself, several ‘at-risk’ factors have been observed: dilution of the doctor-patient relationship, lack of a robust integrated model of the human person and psychopathology, increased commercialisation, excessive reliance on other professionals, disconnection of knowledge generation and transmission, and tension between the healing and public safety roles of psychiatry. The complexity of mental illness, coupled with high stigma and low resources (even in relatively affluent populations), continues to undermine the proper functioning of psychiatry as a medical speciality. These challenges are likely to intensify in the future. Psychiatry as a profession needs to consolidate a robustly integrated medical approach to mental illness that resists splitting into ‘biomedical’ and ‘psychosocial’ perspectives, in the form of a biopsychosocially-informed medical psychotherapeutic practice. It will need to work with other stakeholders in the broader landscape of public mental health without diluting the healing roles in treating mental disorders. Behind a number of recent changes, the ‘invisible hand’ of the market economy is potentially driving psychiatry towards more inequity and escalating costs. Some of these have been fuelled by decreased effectiveness of the conventional academic platform and the rise of new information platforms that are increasingly challenging to manage. A thoughtful, prudent, and coordinated approach by the profession is essential in ensuring a healthy trajectory for the future of mental health care.
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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 | 0.006 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.004 | 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".