Short term global health experiences and local partnership models: a framework
Bibliographic record
Abstract
Contemporary interest in in short-term experiences in global health (STEGH) has led to important questions of ethics, responsibility, and potential harms to receiving communities. In addressing these issues, the role of local engagement through partnerships between external STEGH facilitating organization(s) and internal community organization(s) has been identified as crucial to mitigating potential pitfalls. This perspective piece offers a framework to categorize different models of local engagement in STEGH based on professional experiences and a review of the existing literature. This framework will encourage STEGH stakeholders to consider partnership models in the development and evaluation of new or existing programs.The proposed framework examines the community context in which STEGH may occur, and considers three broad categories: number of visiting external groups conducting STEGH (single/multiple), number of host entities that interact with the STEGH (none/single/multiple), and frequency of STEGH (continuous/intermittent). These factors culminate in a specific model that provides a description of opportunities and challenges presented by each model. Considering different models, single visiting partners, working without a local partner on an intermittent (or even one-time) basis provided the greatest flexibility to the STEGH participants, but represented the least integration locally and subsequently the greatest potential harm for the receiving community. Other models, such as multiple visiting teams continuously working with a single local partner, provided an opportunity for centralization of efforts and local input, but required investment in consensus-building and streamlining of processes across different groups. We conclude that involving host partners in the design, implementation, and evaluation of STEGH requires more effort on the part of visiting STEGH groups and facilitators, but has the greatest potential benefit for meaningful, locally-relevant improvements from STEGH for the receiving community. There are four key themes that underpin the application of the framework: 1. Meaningful impact to host communities requires some form of local engagement and measurement. 2. Single STEGH without local partner engagement is rarely ethically justified. 3. Models should be tailored to the health and resource context in which the STEGH occurs. 4. Sending institutions should employ a model that ultimately benefits local receiving communities first and STEGH participants second. Accounting for these themes in program planning for STEGH will lead to more equitable outcomes for both receiving communities and their sending partners.
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.
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 | 0.015 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.010 | 0.038 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.005 | 0.020 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".