Assessing best practices on short-term medical service trips: an eDelphi-based theoretical model
Bibliographic record
Abstract
Background Short-term, primary-care medical service trips (MSTs) are a controversial modality for addressing the health of marginalised populations and responding to the burden of communicable and non-communicable diseases. As a health-care delivery model, MSTs are challenged by concerns over sustainability, fragmentation of care in host communities, and degree of preparedness among volunteers. Despite the increasing prevalence of such trips, no single framework is routinely used to evaluate their quality. We aimed to develop a literature-based tool for assessing the practices of volunteer MSTs and to validate this tool among stakeholders. Methods We reviewed recent literature to construct a preliminary list of commonly discussed MST best practices. A multidisciplinary panel of academic experts, medical professionals, MST programme coordinators, and non-medical MST volunteers participated in a three-round e-Delphi consensus-building exercise to revise the preliminary list. A 7-point Likert scale was used, with mean scores of 4–7 resulting in rejection of the element, scores less than 2 resulting in acceptance, and scores in between being redistributed for further discussion in rounds two and three. Findings The preliminary framework consisted of 30 elements sorted into six domains: preparedness, impact and safety, efficiency, cost-effectiveness, sustainability, and education. The 26 stakeholders on the eDelphi panel reached consensus on 18 desirable elements to include in the final framework for an effective MST. The elements of the final framework were directly adapted to create a rating scale for medical professionals and trainees to evaluate the practices of volunteer-sending organisations listed in a large online database (http://www.medicalservicetrip.com). Interpretation Evaluation of such practices will allow volunteers to select quality opportunities with effective, sustainable health-care delivery models. Future research should extend this study by initiating a dialogue on best practices between host communities, local clinicians, and MST-sending organisations. Funding None.
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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 | 0.116 | 0.063 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.011 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".