A needs assessment for formal emergency medicine curriculum and training in Zambia
Bibliographic record
Abstract
Emergency medicine (EM) is a nascent field in Zambia. While not yet recognized as a medical specialty, there is national interest for developing more robust emergency care systems in this setting. One key element of strengthening EM in Zambia is identifying current gaps in emergency healthcare provision and opportunities for advancement in the field. This research used a modified version of the Emergency Care Assessment Tool to characterize the landscape of EM in Zambia. We collected data on the extent of EM training and teaching engagement among physicians practicing EM in Zambia. The survey assessed three aspects of core EM "signal functions" among the respondents which included; how often they performed the function, how confident they felt with the function, and how important they deemed the function to be in their practice. Finally, we asked respondents to identify barriers to performing the functions in their departments. The majority of respondents were early in their career, all below the age of 50, and participated in some form of teaching and supervision of learners, with minimal access to teaching resources to enhance their work. There was unanimous agreement with the need for formal postgraduate EM training in Zambia. The EM functions performed least often by EM physicians, and in which they felt the least confident, were high-acuity low-occurrence (HALO) procedures such as surgical airway and pericardiocentesis. The most common barrier to performing an EM function was access to supplies, equipment and medication. The second most commonly cited barrier was healthcare worker training. This research identified several critical needs for EM curricula in Zambia, specifically teaching resources for clinicians who supervise learners, directed learning on HALO procedures, and formal postgraduate training in EM based in Zambia.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".