The retention of ACCESS nursing assistant graduates in rural Uganda
Bibliographic record
Abstract
Background: In 2004, the African Community Center for Social Sustainability (ACCESS) established a Nursing Assistant School in Nakaseke, a rural district in Uganda, to address the region’s severe shortage of healthcare resources. A survey conducted in July 2014 assessed the retention of its graduates in rural healthcare work.Methods: A survey aimed at evaluating the retention of ACCESS graduates in rural areas was created with the help of local stakeholders, focusing on demographics, the training program, employment, career development goals, and community impact. A short-form telephone survey was administered to graduates living outside Nakaseke, and a long-form in-person survey to graduates residing close to the school. Quantitative data was analyzed using standard statistical software, and qualitative data via identification of common themes.Results: Thirty-seven participants were contacted using telephone numbers stored in a database containing information for 109 graduates. The mean participant age was 24 years, and 86.5% were female. Nearly all worked in healthcare (91.1%), primarily in health clinics (37.14%) and pharmacies (33.33%) in communities they described as rural (80%), low-resource (60%), and underserved (25.7%). Most graduates planned to continue working in healthcare (85.3%) in rural areas (61.3%). All felt that their work positively impacts their community.Conclusions: The ACCESS nursing assistant training program provided a stepping stone for trainees while contributing to increased health service provision to the community. Rural-focused location and school curriculum, along with confidence building, may help retain nursing assistant trainees in underserved areas.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".