<scp>U</scp>ganda nursing research agenda: a<scp>D</scp>elphi study
Bibliographic record
Abstract
AIM: Use a Delphi Methodology to identify nursing research priorities in Uganda. BACKGROUND: Identifying nursing research priorities, empowering researchers, and encouraging relevant studies can advance attaining global health goals. The Uganda Nurses and Midwives Union identified the need to establish a nursing research agenda. Nurse leaders have a priority of increasing the influence of nurses in practice and policy. This study was conducted as a preliminary step in a long-term strategy to build nurses' capacity in nursing research. METHODS: A three-round Delphi study was conducted. The 45 study participants were nurses in practice, nurse faculty and members of the Uganda Nurses and Midwives Union. In the initial round, the participants wrote their responses during face-to-face meetings and the follow-up rounds were completed via email. RESULTS: Maternal and child morbidity and HIV/AIDS were identified as research priorities. Nurses also identified nursing practice, education and policy as key areas that nursing research could impact. LIMITATIONS: Demographic characteristics such as length of time in nursing were not collected. Additionally, first round participants completed a pencil-paper survey and the follow-up rounds were done by email. CONCLUSIONS: Nurse Leaders in Uganda identified areas where research efforts could have the most impact and were most relevant to nursing practice. IMPLICATIONS FOR NURSING AND HEALTH POLICY: Health policy decisions have historically been made without nursing input. Nursing research can provide evidence to inform policy and, ultimately, improve population health. The focus of nursing research in priority areas can be used to guide nursing contribution in policy discussions.
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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 | 0.016 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".