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 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 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".