User Feedback on the MSF Tele-Expertise Service After a 4-Year Pilot Trial – A Comprehensive Analysis
Bibliographic record
Abstract
We surveyed all users of the Médecins Sans Frontières (MSF) tele-expertise service, approximately four years after it began operation. The survey contained 50 questions and was sent to 294 referrers and 254 specialists. There were 163 responses (response rate 30%). There were no significant differences between the responses from French and English users, so the responses were combined for subsequent analysis. Most of the responders were doctors (133 of 157 who answered that question), and most had completed field missions for MSF, i.e., both specialists and referrers. The majority stated that the system was user friendly and that they found it self-explanatory (i.e., they did not need to be shown how to use it). Almost all the referrers found that the telemedicine advice that they received was helpful, changed diagnosis and management, and/or reassured the patient. Similar feedback came from the specialists, who also felt that there was educational value for the field doctor. Although there was general satisfaction with the service, the survey identified various problems. The main concerns of the referrers were the lack of promotion of the system at headquarters' level, and the main concerns of the specialists were the lack of feedback about patient follow-up. Nonetheless, both referrers and specialists recognized the benefits of telemedicine in improving patient management, providing education, and reducing isolation in the field.
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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 | 0.029 | 0.074 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".