Low- and Middle-Income Country Perceptions of Global Health Engagements: A Scoping Review
Bibliographic record
Abstract
More than one million Americans are estimated to participate in global health engagements (GHEs) in low- and middle-income countries (LMICs) each year. A growing number of studies document perceptions of GHEs from the perspective of American and other high-income country (HIC) visitors traveling to LMICs, particularly regarding motivations and satisfaction relative to their participation in these activities. Far fewer studies examine perceptions of GHEs from the perspective of LMIC hosts and other local constituent groups. The purpose of this study was to identify and analyze studies that examined local stakeholder perspectives of global health engagements in LMICs around the world. We conducted a scoping review of PubMed and Google Scholar using the Population-Concept-Context (PCC) framework. Assessment and analysis of articles was conducted by a team of three reviewers (EA, FS, SB). A total of 31 relevant papers published between 2009 and 2021 provided local perspectives of GHEs, with participants falling into three stakeholder categories: providers of care, recipients of care, and community members. Analysis revealed that stakeholder groups often held complex and highly nuanced perspectives of GHEs, perceiving these activities as having both positive and negative implications in the host communities. Synthesis of the eligible studies’ findings resulted in three thematic categories: resources and perceived benefits derived from GHEs; perceived challenges associated with GHEs; and opportunities for improvement of GHEs. To our knowledge, this scoping review is among the first to identify and collectively analyze LMIC stakeholder perceptions of GHEs. Recommendations for future research are provided.
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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 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.030 | 0.032 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".