Residents working with Médecins Sans Frontières: training and pilot evaluation
Bibliographic record
Abstract
BACKGROUND: Well-prepared humanitarian workers are now more necessary than ever. Essential to the preparation process are: clearly defined learning objectives, curricula tailored to the nuances of humanitarian settings, simulation-based training, and evaluation. This manuscript describes a training program designed to prepare medical residents for their first field deployment with Médecins Sans Frontières and presents the results of a pilot assessment of its effectiveness. METHODS: The training was jointly developed by the Research Center in Emergency and Disaster Medicine- CRIMEDIM of the Università del Piemonte Orientale, Novara, Italy, and the humanitarian aid organization Médecins Sans Frontières- Italy (MSF-Italy); the following topics were covered: disaster medicine, public health, safety and security, infectious diseases, psychological support, communication, humanitarian law, leadership, and job-specific skills. It used a blended-learning approach consisting of a 3-month distance learning module; 1-week instructor-led coaching; and a field placement with MSF. We assessed its effectiveness using the first three levels of Kirkpatrick's training evaluation model. RESULTS: Eight residents took part in the evaluation. Four were residents in emergency medicine, 3 in anesthesia, and 1 in pediatrics; 3 of them were female and the median age was 31 years. Two residents were deployed in Pakistan, 1 in Afghanistan, 1 in the Democratic Republic of Congo, 1 in Iraq, 2 in Haiti and 1 on board of the MSF Mediterranean search & rescue ship. Mean deployment time was 3 months. The average median score for the overall course was 5 (excellent). There was a significant improvement in post-test multiple choice scores (p = 0.001) and in residents' overall performance scores (P = 0.000001). CONCLUSION: Residents were highly satisfied with the training program and their knowledge and skills improved as a result of participation. TRIAL REGISTRATION: This study was approved by the Institutional Ethics Committee (date 24-02-2016, study code UPO.2015.4.10).
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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 | 0.014 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".