Scaling up pediatric nurse specialist education in Ghana – a longitudinal, mixed methods evaluation
Bibliographic record
Abstract
BACKGROUND: Inadequate health human resources is a key challenge to advancing child survival in Ghana. Nurses are an essential human resource to target because they represent the largest portion of the health workforce. Building on lessons learned from our pilot pediatric nurse training project and World Health Organization guidelines for transforming and scaling up health professional education, this project aimed to; train 500 pediatric nurse specialists through a one-year training program; develop and integrate a critical mass of pediatric nursing faculty and establish a national standardized pediatric nursing curriculum. This study aimed to evaluate the effectiveness of a national pediatric nurse training program in Ghana at the end of 4 years, including eight cohorts with 330 graduates. METHODS: This was a mixed-method evaluation with surveys, focus groups and a pre-test/post-test design. Before and after surveys were used to measure knowledge and confidence at baseline and graduation. Objective Structured Clinical Examinations (OSCE) were used to measure clinical skills at baseline, graduation, and 14 months follow-up. At the end of every module, surveys were used to measure students' satisfaction. Focus groups at graduation qualitatively measured program outcomes. Repeat focus groups and surveys at 14 months after graduation captured the graduates' career progress, experiences reintegrating into the health system and long-term program outcomes. RESULTS: Overall, the graduates completed the program with significantly increased knowledge, confidence, and clinical skills. They also had increased job satisfaction and were able to apply what they learned to their jobs, including leadership skills and gender-sensitive care. Data from 14-month follow-up OSCEs showed that all graduates remained competent in communication, physical assessment, and emergency care, although some obtained a lower mark compared to their performance at graduation. This finding is linked with the observation that the amount of mentorship, support from leadership and equipment that the graduates accessed from their respective facilities varied. CONCLUSIONS: Mixed-methods evaluations demonstrated significant increases in knowledge confidence and skills by completing the program and maintenance of skills more than 1 year after graduation. Findings have implications for those working on the design, implementation, and evaluation of nursing education interventions in low- and middle-income countries.
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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 | 0.027 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".