Core competencies in critical care for general medical practitioners in South Africa: A Delphi study
Bibliographic record
Abstract
Background: Despite a high burden of disease that requires critical care services, there are a limited number of intensivists in South Africa (SA). Medical practitioners at district and regional public sector hospitals frequently manage critically ill patients in the absence of intensivists, despite these medical practitioners having had minimal exposure to critical care during their undergraduate training. Objectives: To identify core competencies in critical care for medical practitioners who provide critical care services at public sector hospitals in SA where intensivists are not available to direct patient management. Methods: A preliminary list of core competencies in critical care was compiled. Thereafter, 13 national and international experts were requested to achieve consensus on a final list of core competencies that are required for critical care by medical practitioners, using a modified Delphi process. Results: A final list of 153 core competencies in critical care was identified. Conclusion: The core competencies identified by this study could assist in developing training programmes for medical practitioners to improve the quality of critical care services provided at district and regional hospitals in SA. Contribution of the study: The study provides consensus on a list of core competencies in critical care that non-intensivist medical practitioners managing critically ill patients in healthcare settings in South Africa, especially where intensivists are not readily available, should have. The list can form the core content of training programmes aimed at improving critical care competence of general medical practitioners, and in this way hopefully improve the overall outcomes of critically ill patients in South Africa.
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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 | 0.015 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.004 |
| 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".