The initiative for medical equity and global health (IMEGH) resuscitation training program: A model for resuscitation training courses in Africa
Bibliographic record
Abstract
In high-income countries, outcomes following in hospital cardiac arrest have improved over the last two decades due to the introduction of rapid response teams, cardiac arrest teams, and advanced resuscitation training. However, in low-income countries, such as Rwanda, outcomes are still poor. This is due to multiple factors including lack of adequate resuscitation training, few trainers, and lack of equipment. To address this issue, the Initiative for Medical Equity and Global Health Equity (IMEGH), a training organization founded in 2018 by 5 local anesthesiologists has regularly taught resuscitation courses such as Basic Life Support, Advanced Cardiac Life Support, and Pediatric Advanced Life Support in hospitals throughout Rwanda. The aims of the organization include developing a sustainable model to offer context relevant resuscitation training courses, building a cadre of local instructors to teach on the courses, as well as engaging funding partners to help support the effort. From October 2018 until September 2022, 31 courses were run in 11 hospitals across Rwanda training 1,060 healthcare providers (mainly of non-physician anesthetists, nurses, midwives, and general practitioners). Ongoing challenges include lack of local protocols, inability to tracking resuscitation outcomes, and continued inaccessibility by many healthcare providers. Despite these challenges, the IMEGH program is an example of a successful context-relevant model and has potential to inform the design of resuscitation programs in other similar settings. This article describes the development of the IMEGH program, accomplishments as well as lessons learned, challenges, and next steps for expansion.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".