The Importance of Militaries from Developing Countries in Global Infectious Disease surveillance/Importance Du Personnel Militaire Des Pays En Developpement Dans la Surveillance Mondiale Des Maladies infectieuses/Importancia De Los Militares De Los Paises En Desarollo En la Vigilancia Mundial De Las Enfermedades Infercciosas
Bibliographic record
Abstract
Introduction Militaries maintain public health programmes to monitor, prevent and treat infections that could reduce the operational effectiveness of their forces. To advance mission objectives or broader national goals, military forces may extend their public health capabilities to civilian populations not adequately served by civilian public health programmes--for example, groups experiencing humanitarian emergencies or people in remote areas beyond the reach of ministries of health. However, the mobility that facilitates such operations can also allow military forces to carry infectious agents to susceptible civilian populations. (1) In many developing countries (that is, low- or middle-income economies as classified by the World Bank) (2) the pursuit of foreign policy goals may involve use of military forces to participate in peacekeeping operations, military exercises and humanitarian relief missions, or to carry out more traditional military tasks such as the securing of borders. Here, we consider the growing importance of developing country militaries in global affairs, and the threats and opportunities this growth presents for infectious disease surveillance and control in civilian populations. We use examples from Peru and Thailand to show how militaries in developing countries can strengthen surveillance programmes run by ministries of heath. Militaries in developing countries: new and traditional missions During the 1990s, military forces in developing countries comprised an increasing proportion of the global total military as the United States and other high-income countries made significant reductions in force size. (3) According to one set of troop strength estimates, militaries in developing countries currently comprise 17 of the 25 largest active duty forces worldwide, with a combined total of 10.5 million of the 14.3 million personnel in these 25 forces. (4) Currently, military forces from developing countries are deployed to participate in many multinational operations (Box 1). Increasing engagement abroad is evident in UN peacekeeping operations. Between 2001 and 2006, the number of high-income countries contributing military forces to UN peacekeeping operations decreased slightly from 24 to 23, and the number of military personnel contributed by high-income countries fell from about 8000 to 2000. During the same period, the number of developing countries contributing military forces increased from 53 to 73, and the number of personnel contributed nearly tripled, from about 22 000 to about 63 000. (5) Box 1. Types and recent examples of multinational military operations with significant participation by developing countries United Nations peacekeeping operations (current) United Nations Organization Mission in the Democratic Republic of the Congo; 18 473 military personnel. United Nations Mission in Liberia; 15 638 military personnel; 14 other current operations Other multinational peacekeeping or security operations (current) African Union Mission in Sudan; 7000 military personnel. Multinational Force and Observers, Sinai Peninsula; 1500 military personnel. Operation Iraqi Freedom and Operation Enduring Freedom Joint military exercises with the United States Panamax 2006:18 countries; Panama Canal defence. Cobra Gold, 2006, with Indonesia, Japan, Singapore, Thailand; various scenarios. Natural Fire, 2006, with Kenya, Uganda, the United Republic of Tanzania; humanitarian response coordination. Humanitarian relief operation Indian Ocean tsunami response, 2004--05; militaries from about 20 countries. Developing countries also supply forces to non-UN multinational missions. The African Union Mission in Sudan draws its approximately 7000 military personnel from Chad, Egypt, Gabon, the Gambia, Kenya, Nigeria, Rwanda, Senegal, South Africa and Zambia. (6) The Multinational Force and Observers in the Sinai Peninsula includes about 800 military personnel from Colombia, Fiji and Uruguay, in addition to contingents from Canada, European countries, New Zealand and the United States. …
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".