Perspective: Postearthquake Haiti Renews the Call for Global Health Training in Medical Education
Bibliographic record
Abstract
On January 12, 2010, Haiti experienced one of the worst disasters in human history, a magnitude 7.0 earthquake, resulting in the deaths of approximately 222,000 Haitians and grievous injury to hundreds of thousands more. International agencies, academic institutions, nongovernmental organizations, and associations responded by sending thousands of medical professionals, including nurses, doctors, medics, and physical therapists, to support the underresourced Haitian health system. The volunteers who came to provide medical care to disaster victims worked tirelessly under extremely challenging conditions, but in many cases they had no previous work experience in resource-limited settings, minimal training in tropical disease, and no knowledge of the historical background that contributed to the catastrophe. Often, this lack of preparedness hindered their ability to care adequately for their patients. The authors of this perspective argue that the academic medicine community must prepare medical trainees not only to treat the illnesses of patients in resource-limited settings but also to fight the injustice that fosters disease and allows such catastrophes to unfold. The authors advocate purposeful attention to building global health curricula; providing adequate time, funding, and opportunity to work in resource-limited international settings; and ensuring sufficient supervision for trainees to work safely. They also call for an interdisciplinary approach to global health that both affirms health care as a fundamental human right and explores the historical, economic, and political causes of inequitable health care.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.008 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.023 | 0.023 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".