Building capacity for nurse‐led research
Bibliographic record
Abstract
AIM: To discuss factors that have influenced the development of research capacity among nurses in lower and middle-income countries (LMICs). BACKGROUND: Concerned health scientists have addressed the importance of building research capacity among health professionals. Strengthening capacity specifically among LMIC nurses has been infrequently discussed. Without the requisite educational preparation or an enabling environment for research, nurses are unlikely to either demand research capacity-building opportunities or initiate research examining nursing practice and health system challenges. METHODS: A scan was conducted of nine internationally funded research capacity-building initiatives to identify programme targeting and the proportion of nurse trainees. A literature review examined graduate and post-graduate training opportunities for LMIC nurses, and barriers and enablers to nurses' involvement in research. Informal consultations were held with nurse leaders in 15 LMICs and leaders of eight LMIC nursing organizations. FINDINGS: The scan found a generic targeting of health professionals with a very low percentage of nurse trainees. Programmes specifically targeting nurses did attract and prepare a significant number of nurses. Factors limiting nurses' involvement in research include hierarchies of power among disciplines, scarce resources, a lack of graduate and post-graduate education opportunities, few senior mentors, and prolonged underfunding of nursing research. CONCLUSIONS: Fully engaging LMIC nurses in health services research may yield pragmatic and evidence-informed service delivery and policy recommendations. Investments in supports for nursing research capacity may enrich global health policy effectiveness and improve quality of care.
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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 | 0.160 | 0.190 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.005 | 0.030 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.003 |
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".