Training of primary care nurses in addressing noncommunicable diseases prevention, surveillance, and management: Endline evaluation of an implementation research project conducted in Hwange District, Zimbabwe
Bibliographic record
Abstract
Background: Noncommunicable diseases (NCDs) cause over 70% of global deaths, with 33% of deaths in Zimbabwe attributed to NCDs. Rural districts face severe workforce and resource shortages. We evaluated an effective end–line training program for primary care nurses (PCNs) in Hwange District to strengthen NCD prevention, surveillance, and management. Materials and Methods: In March 2025, 14 nurses from seven primary clinics completed a posttraining survey. A validated questionnaire (Cronbach’s α =0.89) assessed self-rated competency (1–4) across six prevention, six surveillance, and five management domains, and perceptions of module usefulness and healthsystem readiness on a 5-point Likert scale. We calculated descriptive statistics, reliability, normality (Shapiro–Wilk), and U -tests for gender comparisons. Ethical approval was obtained and consent provided. Results: Participants were 71.4% female, mean age 44.1 ± 6.1 years with 14.0 ± 4.1 years of experience. High self-rated competency in prevention was highest for alcohol use and diet (71.4% each) and lowest for tobacco use and mental health (57.1% each). Surveillance knowledge peaked for diet (78.6%) and was lowest for alcohol (50.0%). Management competency was highest for hypertension (71.4%) and lowest for cancer (21.4%) and heart disease (35.7%). Despite high module usefulness ratings (78.6%–92.9%), 71.4% reported insufficient resources and 85.7% reported medication stockouts. No gender differences emerged ( U = 27.0, P = 0.346). Conclusion: These findings provide actionable evidence to support the scale-up of nurse-led NCD services in line with national strategies and global goals. Task-shifting NCD care to PCNs is feasible and enhances self-reported competencies. To sustain rural services, targeted mentorship, supply-chain strengthening, and focused training on tobacco control, mental health, and complex disease management are essential using implementation research.
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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".