Principles to guide a volunteer humanitarian faith-based short-term medical mission in Nepal: A case study
Bibliographic record
Abstract
Global health inequities, natural disasters, and mass migration of refugees have led to an increase in volunteer humanitarian responses worldwide. While well intentioned for doing good, there is an increasing awareness of the importance for improved preparation for international volunteers involved in short-term medical missions (STMMs). This case study describes the retrospective application of Lasker’s (2016) Principles for Maximizing the Benefits for Volunteer Health Trips to international volunteers from two faith-based non-governmental organizations (NGOs) in Canada and the United States partnering with a faith-based NGO in Nepal. These principles are intended to maximize the benefits and diminish challenges that may develop between the international volunteers and the host country staff. Lessons from this case study highlight the importance of applying such principles to foster responsible STMMs. In conclusion, there is an increasing call by host country staff for collaborative and standardized guidelines or frameworks for STMMs and other global health activities.
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 | 0.014 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.006 | 0.007 |
| 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".