The Addis Ababa toxicology curriculum project: educational needs assessment for the toxicology modules of an emergency medicine training program
Bibliographic record
Abstract
BACKGROUND: The Toronto Addis Ababa Academic Collaboration in Emergency Medicine (TAAAC-EM) is a bi-institutional partnership between the University of Toronto (UofT) and Addis Ababa University (AAU) focused on addressing the need for emergency medicine (EM) postgraduate training and care in Ethiopia. Toxicology is a key competency in EM. EM physicians are often the first and sole clinicians to identify and treat patients presenting with a wide range of intoxications. The goal of this project was to conduct an educational needs assessment to inform the development of a context-specific toxicology curriculum for the AAU EM training program. METHODS: Our needs assessment employed a survey (available electronically and in paper format) and face-to-face interviews conducted with Ethiopian EM faculty (all graduates of the AAU EM residency training program) and current AAU EM residents. The survey was distributed in October 2018 and the interviews were conducted in November 2018. RESULTS: Of the 63 surveys distributed, we received 17 complete responses and completed 11 interviews with AAU EM faculty and residents. The survey conducted on toxicology training highlighted overall satisfaction with current training, with thematic analysis revealing key areas for growth. System-related themes focused on resource availability, healthcare access, and public health education. Provider-related themes emphasized the need for context-specific training, including common local toxins, and for advanced toxicology training such as poison center rotations. Patient-related themes centered on specific toxicological presentations in Ethiopia, highlighting the importance of public health advocacy, education on safe handling, and governmental regulation of toxic substances. Both survey and interview data highlighted challenges stemming from inconsistent availability of resources and underscored the need for tailored education to manage poisoned patients with locally available resources. CONCLUSIONS: Our findings indicate the need to focus on the most prevalent local toxicological presentations and practical management challenges in local contexts, including resource limitations and delayed presentations. Moreover, it emphasizes the importance of public health initiatives such as regulation of the sale and promotion of safe handling of toxic substances to mitigate toxicological risks. These findings are likely relevant to other resource-constrained settings outside of Ethiopia.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".