How to Develop a Sustainable Program for Volunteer Medical Care in Low- and Middle-Income Countries
Bibliographic record
Abstract
Volunteer medical missions to low- and middle-income countries provide necessary care to underserved populations (Meara et al., Lancet 386(9993):569–624, 2015). For almost thirty years, our team (directed by DH) has been operating at the Hospital Universitario Hernando Moncaleano Perdomo, a teaching hospital for the Surcolombiana University Faculty of Health in Neiva, Colombia. Neiva, with a population of approximately four hundred thousand, is the capital of the Department of Huila, which has a population of just over one million. Neiva is located approximately one hour by air south of Bogotá, the country’s capital. At the start, we had a team of approximately twenty healthcare providers. This has grown to over one hundred individuals from Neiva and other cities in Colombia, the United States, Canada, and several countries in Europe. The following is an exposition on how such effort starts, what is needed to initiate it and what is most important, how to sustain such effort and expand it over many years. The goal of such an effort is to transfer and expand the knowledge and skills and to support it with the local healthcare professionals and with the local institutions and the local public. Our collaboration would not have been successful without the continuous support of one individual, Carlos Fajardo, a retired civil engineer. He was the catalyst that brought our first team to Neiva in 1993 and has been instrumental in handling all the local logistics. He has been the perfect champion – serving as a fierce advocate for children’s health and a mentor and friend to many of us.
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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 | 0.021 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.003 | 0.023 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.028 | 0.011 |
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".