(A100) Building Local Resilience and Competencies in Remote Haitian Communities
Bibliographic record
Abstract
Injury and trauma are major causes of premature deaths worldwide. At present, Haiti does not have an existing emergency medical system. Basic first responders training was developed for lay people and medical professionals in rural Haiti. Methods The training was conducted in Terrier Rouge, Haiti. Participants included medical professionals, laborers, health professionals, teachers, students, and truck drivers from six towns in northeastern Haiti. A three-day training course taught by U.S. board certified emergency medicine physicians was instituted. Basic life support (BLS), first aid, and BLS/first aid instructors courses were taught based on the American and Canadian Heart Associations curriculum. The BLS/first aid instructors course was limited to health professionals, whereas the first aid course was open to all members of the community. The program included the development of local teaching tools and manuals translated to local languages. Twelve newly trained local Haitian instructors assisted in the final day of training. Results The course was well received by participants. A total of 54 people completed the BLS course, 67 completed the first aid course, and 12 participants completed the BLS/first aid instructors course. Ninty-five program participants completed the end of course survey. Forty-four of the participants were male, 49 were female, and 2 did not answer. Forty-one participants had no prior BLS/first aid training or exposure. The ages of participants ranged from 13 to 52 years. The course participants included two physicians, 22 students, eight nursing students, seven nurses, 20 teachers, 12 health workers, five drivers, and 14 laborers. Of those surveyed, 92 stated they would recommend this course to a friend. Eighty-eight participants stated that hands on learning helped them better learn the course material. Conclusion This sustainable, locally controlled training model increased local skill level for basic first responders in rural Haiti.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.031 | 0.005 |
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".