Building capacity for nurse‐led research
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.006 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it