EXPLORING PERINATAL GLOBAL HEALTH: A REFLECTIVE COMMENTARY OF A NURSING STUDENT’S EXPERIENCE ABROAD IN UGANDA
Bibliographic record
Abstract
Background: The first author undertook the Perinatal Global Health Internship from May to July 2016 in Kampala, Uganda as part of the Canadian Queen Elizabeth II Diamond Jubilee Scholarship Program funded by the Community Foundations of Canada. The internship was carried out in partnership with the University of Calgary, Faculty of Nursing and University of Calgary International, and the Aga Khan University – School of Nursing and Midwifery with technical support from Universities Canada. Aim: In this paper, we explain the role of nursing in global health, explore the first author’s learning in the area of perinatal health, and invite other nursing students to engage in global health work. Discussion of Stories: A reflective commentary is used to describe the first author’s experience in a government hospital in Kampala as she learned to recognize the implications of perinatal distress, socio-ecological conditions, and resource-poor settings on the health of mothers and premature neonates. In the commentary, the first author also describes the development of an Early Childhood Development resource and the value of partnership in relation to this experience. Reflection: The first author reflects on the benefit of the internship in developing key competencies and attributes for global health work, the need for cultural competency, and barriers to creating effective change to address complex issues. Conclusion: The first author summarizes key learning from the practice, teaching, and research components of the internship. She describes growth, two-way learning, and recommendations for the internship.
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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 | 0.010 | 0.045 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.014 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.012 | 0.026 |
| Insufficient payload (model declined to judge) | 0.002 | 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".