Professorships in child and adolescent psychiatry relative to a similarly sized medical specialty in the UK and Ireland: cross-sectional study
Bibliographic record
Abstract
BACKGROUND: A youth mental health crisis is considered one of the great challenges of our time, and research and clinical services in child and adolescent psychiatry have become a priority for governments and funders. Academic leadership is needed to drive forward research. It is not clear how many senior academic leadership posts (professorships) there are in child and adolescent psychiatry, nor how this benchmarks against a similarly sized medical specialty. AIMS: This study aimed to determine the number of professorships in child and adolescent psychiatry in the UK and Ireland compared to a similarly sized specialty. A secondary aim was to identify the number of clinical trials registered for mental and behavioural disorders in children. METHOD: We identified registered specialists in child and adolescent psychiatry and a similarly sized specialty who held full professorships in medical schools. We searched the International Standard Randomised Controlled Trial Number (ISRCTN) and ClinicalTrials.gov for trials. RESULTS: = 1724). We identified 24 professors in child and adolescent psychiatry across the UK and Ireland, compared to 124 in neurology. For every intervention trial registered for mental and behavioural disorders in children, there were approximately ten trials registered for diseases of the nervous system. CONCLUSIONS: Despite equivalent numbers of medical specialists in child and adolescent psychiatry and neurology, there is a striking disparity in the number of professorship appointments. While young peoples' mental health has, ostensibly, become a priority for policy-makers and funders, this is not reflected in medical professorship appointments. The paucity of senior academic child and adolescent psychiatrists has real-world implications for training, research, innovation and service development in mental health services.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".