Evaluation of the readiness of nurse anesthetists in Ghana for a master’s degree completion program: An exploratory, observational study
Bibliographic record
Abstract
Ghana is a developing country in West Africa with limited anesthesia providers impacting surgical access and anesthesia safety. There are only two anesthesia providers per 100,000 population, with the majority of providers being nurse anesthetists, most of whom hold only diplomas, and more recently, bachelor’s in anesthesia education. This paper reports an observational study exploring the prospects of an advanced degree at the master’s degree level for practicing nurse anesthetists in Ghana. Three focus groups and one semi-structured individual interview were conducted with a total of 69 participants. Four major themes emerged following data analysis: desire for improved clinical expertise; focus on research methods to improve patient outcomes; perceived inadequate physician support for graduate nurse anesthetists (NA) education and infrastructure; and desires for an advanced degree for career progression. Findings highlight the readiness of nurse anesthetists in Ghana for an advanced degree and the necessary infrastructure and needed areas of clinical anesthesia education and research that must be included in the curriculum development for a master’s level education.
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 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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".