Brain Gains: a literature review of medical missions to low and middle-income countries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BACKGROUND: Healthcare professionals' participation in short-term medical missions to low and middle income countries (LMIC) to provide healthcare has become common over the past 50 years yet little is known about the quantity and quality of these missions. The aim of this study was to review medical mission publications over 25 years to better understand missions and their potential impact on health systems in LMICs. METHODS: A literature review was conducted by searching Medline for articles published from 1985-2009 about medical missions to LMICs, revealing 2512 publications. Exclusion criteria such as receiving country and mission length were applied, leaving 230 relevant articles. A data extraction sheet was used to collect information, including sending/receiving countries and funding source. RESULTS: The majority of articles were descriptive and lacked contextual or theoretical analysis. Most missions were short-term (1 day - 1 month). The most common sending countries were the U.S. and Canada. The top destination country was Honduras, while regionally Africa received the highest number of missions. Health care professionals typically responded to presenting health needs, ranging from primary care to surgical relief. Cleft lip/palate surgeries were the next most common type of care provided. CONCLUSIONS: Based on the articles reviewed, there is significant scope for improvement in mission planning, monitoring and evaluation as well as global and/or national policies regarding foreign medical missions. To promote optimum performance by mission staff, training in such areas as cross-cultural communication and contextual realities of mission sites should be provided. With the large number of missions conducted worldwide, efforts to ensure efficacy, harmonisation with existing government programming and transparency are needed.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it