Bibliographic record
Abstract
INTRODUCTION: Ethiopia is a country of 81 million people, half of whom are children. The prevalence of psychiatric disorders in children ranges from 3.5-23.2%. However, there are very limited mental health resources in the country, including few psychiatrists. Thus the training of more psychiatrists, including providing them with expertise in child psychiatry, is an imperative. METHOD: The article briefly reviews the development of the Toronto Addis Ababa Psychiatry Project (TAAPP), a collaborative program between the University of Toronto and Addis Ababa University designed to help train psychiatry residents in Ethiopia. The article then focuses on the author's experiences on one recent trip to Ethiopia to provide some of this training. RESULTS: Formal teaching sessions as well as clinical supervision were provided to the Ethiopian residents. Content had to be adapted to be relevant to the Ethiopian context, but teaching approaches did not have to be modified significantly. The Ethiopian residents were very enthusiastic learners and made quick changes to their practices based on the teaching. CONCLUSION: Collaborative programs such as TAAPP may be important mechanisms to improve the training of psychiatrists internationally, especially when there are limited local educational resources.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 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".