Anesthesia Provider Training and Practice Models: A Survey of Africa
Bibliographic record
Abstract
BACKGROUND: In Africa, most countries have fewer than 1 physician anesthesiologist (PA) per 100,000 population. Nonphysician anesthesia providers (NPAPs) play a large role in the workforce of many low- and middle-income countries (LMICs), but little information has been systematically collected to describe existing human resources for anesthesia care models. An understanding of existing PA and NPAP training pathways and roles is needed to inform anesthesia workforce planning, especially for critically underresourced countries. METHODS: Between 2016 and 2018, we conducted electronic, phone, and in-person surveys of anesthesia providers in Africa. The surveys focused on the presence of anesthesia training programs, training program characteristics, and clinical scope of practice after graduation. RESULTS: One hundred thirty-one respondents completed surveys representing data for 51 of 55 countries in Africa. Most countries had both PA and NPAP training programs (57%; mean, 1.6 pathways per country). Thirty distinct training pathways to become an anesthesia provider could be discriminated on the basis of entry qualification, duration, and qualification gained. Of these 30 distinct pathways, 22 (73%) were for NPAPs. Physician and NPAP program durations were a median of 48 and 24 months (ranges: 36-72, 9-48), respectively. Sixty percent of NPAP pathways required a nursing background for entry, and 60% conferred a technical (eg, diploma/license) qualification after training. Physicians and NPAPs were trained to perform most anesthesia tasks independently, though few had subspecialty training (such as regional or cardiac anesthesia). CONCLUSIONS: Despite profound anesthesia provider shortages throughout Africa, most countries have both NPAP and PA training programs. NPAP training pathways, in particular, show significant heterogeneity despite relatively similar scopes of clinical practice for NPAPs after graduation. Such heterogeneity may reflect the varied needs and resources for different settings, though may also suggest lack of consensus on how to train the anesthesia workforce. Lack of consistent terminology to describe the anesthesia workforce is a significant challenge that must be addressed to accelerate workforce research and planning efforts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".