A Study to Determine How Frequently Patients Are Admitted to Acute General Hospitals With Psychiatric Presentations and Whether Acute Medics Feel Confident in Managing Such Cases
Bibliographic record
Abstract
Aims Mental Health Trusts have seen significant funding cuts in recent years resulting in higher admissions to acute medical hospitals due to psychiatric disorders. Little information is available on the quantity of such presentations and no studies have explored how confident acute medical doctors feel in managing patients with mental health disorders. Primary objective: To evaluate whether acute medical doctors feel confident in the common psychiatry topics required to manage patients presenting to medical hospitals with mental health disturbances. Secondary objective: To determine how frequently patients with mental health disorders are admitted to medical beds, either primarily due to their psychiatric disorder or due to another medical problem. Methods Acute medical doctors working in Merseyside, UK completed a self-report survey in which they rated their confidence level in relation to common psychiatric topics. Admission data for 4 large hospitals in Merseyside were analysed to determine the proportion of all patients admitted to medicine in a 1-year period who had a mental health disorder. Results were further broken down into primary diagnosis by ICD–11 code to determine which mental health conditions presented most frequently to general medical hospitals. Results 10 acute medical registrars and 33 acute medical consultants completed the survey. Most acute medical doctors felt at least partly confident in their psychiatry knowledge. However, around a quarter of doctors lacked confidence in managing psychotropic medications and performing risk assessments, with a third of acute doctors unsure how to access specialist psychiatric advice. 43.8% of all medical admissions had a mental health disorder. This was comprised of 3.1% who presented primarily due to a mental health illness, and 40.7% who had a mental health disorder but attended for a different reason. Substance misuse accounted for a significant proportion of these admissions. Conclusion Despite almost half of patients admitted to medical beds experiencing mental illness, many acute medical doctors lack confidence managing psychiatric ailments and half of the respondents felt their medical training has not prepared them sufficiently. In addition, many doctors are unsure how to access specialist advice when needed. This leaves both doctors and patients at risk of harm and suggests a need for additional psychiatric training for acute medical doctors and improved access to support.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".