Building Research Capacity : a Case Study About Maternal-Child Health Researchers in Nigeria and Canada
Bibliographic record
Abstract
Background: To achieve the third Sustainable Development Goal (SDG) of good health and well-being, particularly as it relates to maternal-newborn health, research and policy in LMICs must reflect and account for context specific health needs. It is imperative that midwives, nurses, and other health care providers are therefore equipped with the research and advocacy tools necessary to respond and account for such needs.Purpose: To share the preliminary findings of a case study which explored resiliency and capacity building within the context of collaborative partnerships between maternal-child health researchers in Canada and Nigeria.Methods: Stakeu2019s (1995) principles of qualitative case study guided this research. Following ethical approval, data was collected using individual face to face and skype interviews. Interviews were conducted with key stakeholders located in Nigeria and Canada. Interviews were transcribed verbatim and thematic analysis was used for analysis. Findings: Findings will be presented as they relate to the ongoing collaborative relationships between maternal-child health researchers in Nigeria and Canada. Presented findings will address; strengths of the relationships, challenges of conducting global maternal-child health research, and ideas to enhance the capacity of maternal-child health research in LMICs.Conclusions: Ongoing global partnerships and mentoring are crucial for building research capacity in LMICs. Resiliency and leadership are key to the maintenance of such relationships and mentoring. Implications for practice: These findings have the potential to inform policies that aim to strengthen maternal-child health research globally.
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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 | 0.017 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.006 |
| Science and technology studies | 0.097 | 0.021 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.005 | 0.014 |
| Research integrity | 0.008 | 0.009 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".