Bibliographic record
Abstract
OBJECTIVE: Given striking advances in translational developmental neuroscience and its convergence with developmental psychopathology and developmental epidemiology, it is now clear that mental illnesses are best thought of as neurodevelopmental disorders. This simple fact has enormous implications for the nature and organization of psychotherapy for mentally ill children, adolescents and adults. METHOD: This article reviews the 'trajectory' of psychosocial interventions in pediatric psychiatry, and makes some general predictions about where this field is heading over the next several decades. RESULTS: Driven largely by scientific advances in molecular, cellular and systems neuroscience, psychotherapy in the future will focus less on personal narratives and more on the developing brain. In place of disorders as intervention targets, modularized psychosocial treatment components derived from current cognitive-behavior therapies will target corresponding central nervous system (CNS) information processes and their functional behavioral consequences. Either preventive or rehabilitative, the goal of psychotherapy will be to promote development along typical developmental trajectories. In place of guilds, psychotherapy will be organized professionally much as physical therapy is organized today. As with other forms of increasingly personalized health care, internet-based delivery of psychotherapy will become commonplace. CONCLUSION: Informed by the new field of translational developmental neuroscience, psychotherapy in the future will take aim at the developing brain in a service delivery model that closely resembles the place and role of psychosocial interventions in the rest of medicine. Getting there will be, as they say, interesting.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".