Psychiatry 2050: from younger psychiatrists' perspective
Bibliographic record
Abstract
Psychiatry 2050: from younger psychiatrists' perspective Tariq Mahmood Hassan, Wasif Habib, Mir Nadeem Mazhar, Tariq Munshi Department of Psychiatry, Queen's University, Kingston, ON, CanadaThere have been various opinion pieces on predicting the future of psychiatry and addressing its different domains. This editorial addresses the topic from the vantage point of neuroscientific inquiry. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition (DSM 5) however continues with the tradition of its predecessor (DSM 4 text revision [TR]), addressing most diagnoses with descriptive phenomenology as opposed to attempting to change diagnoses based on causative phenomenology or response to treatment. Advances in genomics and imaging, with time, will hopefully help shape psychiatric diagnoses and classifications with a primary basis on morphology. This may in turn help improve the recruitment of academic psychiatrists to the field. In doing so, the profession will gain respect amongst its peers in other disciplines of medicine and cement its future.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.017 | 0.029 |
| Insufficient payload (model declined to judge) | 0.021 | 0.007 |
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".