Haiti Acute and Emergency Care Conference: descriptive analysis of an acute care continuing medical education program
Bibliographic record
Abstract
# Background Several disasters over the past decade have highlighted the need for strong acute-care systems in Haiti. As part of a multifaceted approach to improving national acute-care training, the Research and Education consortium for Acute Care in Haiti (REACH) launched the inaugural Haiti Acute and Emergency Care Conference (HAECC). # Methods REACH is a Haitian-led, multinational collaboration based out of Saint Lûc Hospital in Port-au-Prince. The first HAECC was held in April, 2017. Pre- and post-course evaluation consisted of subjective and objective components. Differences between pre- and post-responses were determined using McNemar's test of paired proportions. # Results 57 participants from 21 hospitals in five Haitian departments were included. The majority (37/58, 63.8%) were physicians. Most (33/57, 57.9%) had no prior training in acute or emergency care, but 8/57 (14.0%) had taken ATLS/ACLS, 11/57 (19.3%) had taken a formal course not internationally recognized, and only 1/57 (1.8%) had completed acute care specialty training. 43.7% (25/57) reported routine access to point-of-care ultrasound. Following course completion, participants felt significantly more comfortable managing basic emergency conditions (up to 25/57, 43.9% increase, *P*\<0.01) and using ultrasound (up to 30/57, 52.6% increase, *P*\<0.01), but improvements on objective testing were not significant (ranging from -2 (-3.5%, *P*=1.00) to +5 (8.7%, *P*=0.15) change). # Conclusions While continued quality review is necessary for future iterations of the conference, the inaugural HAECC provided a useful "first pass" for current front-line providers in Haiti.
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How this classification was reachedexpand
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".