Learning from developing countries in strengthening health systems: an evaluation of personal and professional impact among global health volunteers at Addis Ababa University’s Tikur Anbessa Specialized Hospital (Ethiopia)
Bibliographic record
Abstract
BACKGROUND: The positive impact of global health activities by volunteers from the United States in low-and middle-income countries has been recognized. Most existing global health partnerships evaluate what knowledge, ideas, and activities the US institution transferred to the low- or middle-income country. However, what this fails to capture are what kinds of change happen to US-based partners due to engagement in global health partnerships, both at the individual and institutional levels. "Reverse innovation" is the term that is used in global health literature to describe this type of impact. The objectives of this study were to identify what kinds of impact global partnerships have on health volunteers from developed countries, advance this emerging body of knowledge, and improve understanding of methods and indicators for assessing reverse innovation. METHODS: The study population consisted of 80 US, Canada, and South Africa-based health care professionals who volunteered at Tikur Anbessa Specialized Hospital in Ethiopia. Surveys were web-based and included multiple choice and open-ended questions to assess global health competencies. The data were analyzed using IBRM SPSS® version 21 for quantitative analysis; the open-ended responses were coded using constant comparative analysis to identify themes. RESULTS: Of the 80 volunteers, 63 responded (79 percent response rate). Fifty-two percent of the respondents were male, and over 60 percent were 40 years of age and older. Eighty-three percent reported they accomplished their trip objectives, 95 percent would participate in future activities and 96 percent would recommend participation to other colleagues. Eighty-nine percent reported personal impact and 73 percent reported change on their professional development. Previous global health experience, multiple prior trips, and the desire for career advancement were associated with positive impact on professional development. CONCLUSION: Professionally and personally meaningful learning happens often during global health outreach. Understanding this impact has important policy, economic, and programmatic implications. With the aid of improved monitoring and evaluation frameworks, the simple act of attempting to measure "reverse innovation" may represent a shift in how global health partnerships are perceived, drawing attention to the two-way learning and benefits that occur and improving effectiveness in global health partnership spending.
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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 | 0.027 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| 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".