Nurses leadership in research and policy in Nigeria: A myth or reality?
Bibliographic record
Abstract
AIM: This study evaluates nurses' leadership in research and policy formulation in southern Nigeria. BACKGROUND: In Africa and particularly in low- and middle-income countries, expected health information from nurse's leaders is sometimes not available, thereby hindering the attainment of sustainable health. METHODS: This qualitative study used 12 high-ranking nurses leader from primary, secondary and tertiary health care systems in Cross River State, Nigeria. In-depth interview and focus group discussion were used to generate and validate collected data. RESULTS: There was marginal leadership in research and policy formulation. The hindering factors were mainly individual and institutional barriers. CONCLUSION: Nurses effective leadership in research and policy have not yet been actualized. Suggested remedies include mentoring of the mentee in research, provision of designated grants for nursing research, acceptance of nurses as policy formulators rather than implementers among others. The small sample size informs the need for further study throughout the region. IMPLICATIONS FOR NURSING MANAGEMENT: Nurses have the capability to exercise influence directly or indirectly on health care goals. Dereliction in research and policy formulation could hinder the attainment of desired health care reforms due to absence of innovation in nursing practice and management.
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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 | 0.021 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| 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".