Academic Affiliated Training Centers in Humanitarian Health, Part I: Program Characteristics and Professionalization Preferences of Centers in North America
Bibliographic record
Abstract
Abstract The collaborative London based non-governmental organization network ELRHA (Enhancing Learning and Research for Humanitarian Assistance) supports partnerships between higher education institutions and humanitarian organizations worldwide with the objective to enhance the professionalization of the humanitarian sector. While coordination and control of the humanitarian sector has plagued the response to every major crisis, concerns highlighted by the 2010 Haitian earthquake response further catalyzed and accelerated the need to ensure competency-based professionalization of the humanitarian health care work force. The Harvard Humanitarian Initiative sponsored an independent survey of established academically affiliated training centers in North America that train humanitarian health care workers to determine their individual training center characteristics and preferences in the potential professionalization process. The survey revealed that a common thread of profession-specific skills and core humanitarian competencies were being offered in both residential and online programs with additional programs offering opportunities for field simulation experiences and more advanced degree programs. This study supports the potential for the development of like-minded academic affiliated and competency-based humanitarian health programs to organize themselves under ELRHA's regional “consultation hubs” worldwide that can assist and advocate for improved education and training opportunities in less served developing countries. Burkle Jr FM, Walls AE , Heck JP , Sorensen BS , Cranmer HH , Johnson K , Levine AC , Kayden S , Cahill B , VanRooyen MJ . Academic affiliated training centers in humanitarian health, Part 1: program characteristics and professionalization preference of centers in North America . Prehosp Disaster Med . 2013 : 28 ( 2 ): 1 - 8 .
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".