Integration of mental health comorbidity in medical specialty programs in 20 countries
Bibliographic record
Abstract
METHODS: A systematic analysis was performed of the medical specialization academic programs of 20 different countries to establish which medical specialties take into account mental health issues in the specialty curricular design and which mental health content these programs address. The criteria that were explored in the educational programs include: 1) name of the medical specialties that take into account mental health content in curriculum design, 2) name of the mental health issues addressed by these programs. After independent review and data extraction, paired investigators compared the findings and reached consensus on all discrepancies before the final presentation of the data. Descriptive statistics evaluated the frequency of the data presented. RESULTS: Internal medicine, family medicine, neurology, pediatrics and geriatrics were the specialties that included mental health topics in their programs. In four countries: Bangladesh, Serbia, the Netherlands and France, 50%of all graduate specialty training programs include mental health content. In ten countries: Germany, Sweden, the United Kingdom, Mexico, Belgium, India, Russia, Canada, Israel and Spain, between 20% and 49% of all graduate specialty training programs include mental health content. In six countries - Brazil, Chile, Colombia, Croatia, Kenya, and the United States-less than 20% of all graduate specialty training programs include mental health content. DISCUSSION: The proposal that we have made in this article should be taken into account by decision-makers, in order to complement the different postgraduate training programs with mental health issues that are frequently present with other physical symptoms. It is not our intention that the different specialists know how to treat psychiatric comorbidities, but rather pay attention to their existence and implications in the diagnosis, evolution and prognosis of many other diseases. The current fragmentation of medicine into ever finer specialties makes the management of comorbidity ever more difficult: a reorientation of post- graduate training might improve the situation.
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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 | 0.010 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".