Bibliographic record
Abstract
Healthcare Under Fire: Stories from Healthcare Workers During Armed Conflict 13 asked for help with two things. I wanted to know what happened to the team and how to save them. The first request was met with appeasement, the second with hope for the best. Eventually, every organization had its limits and mandates. None of them had the mandate to save trapped data collectors in a village that was thought to be safe when randomly selected. Under fire, embarrassingly little is certain and what can be done is even less. Those were the hardest five days in the field. The task at hand was not only about finding my missing children but about keeping the survey running by the other teams who had to travel outside Nyala. I could see the fear in their eyes and feel it in their words. They had to make the hard choice between risking their lives and the payment they received that was at least four-fold what they would get from their governmental jobs. Finally, a call came. It was the one I was waiting for. The team leader told me in a tired voice, made even worse by the terrible signal that made his voice sound as if it were coming from a cave, that they managed to escape the village. They were all physically safe and he spared me the uncomfortable task of asking about the survey data by adding, ‘And we have the filled questionnaires with us.’ I cannot recall any comparable moment of relief. I called all the worried mothers and when the team arrived a day later, I joined them at each of their houses. No words could describe the feelings, the tears of joy, and the gaze of blame when the mothers saw their children safe. I gave them a break before asking them if they wanted to continue with the survey. I had to have an eye on the progress, the decaying budget spent on the daily payments, per diems, rentals, etc. and handle the growing feelings of concern. The headquarters in Khartoum was generous enough to send me an extra budget and a week’s extension. Seems like a happy end, right? I am not sure if a completed survey and well-paid yet traumatized young men and women counts as one. I had to move on and fly back to Khartoum, according to the plan for data entry and data analysis. The final reports had all the numbers the United Nations and the government needed. Very few people knew what the stories behind each of these numbers were. Even fewer people cared to know what the story is. We went to do a well-paid job and we did. When I returned to my office in Khartoum, one of my welcoming colleagues tried to tease me by saying, “Welcome the Lord of War!” with a smile on his face hinting at the generous payment I received. I smiled back and said, “You are right. I feel like one, but I bet you Nicholas Cage was paid much more.” I was referring to the movie that starred him with the same name. What made me feel less of a ‘Lord of War’ was a promise I gave to the people I left behind to make sure their stories remain alive and not hidden between the lines of the graphs in the report of the next survey.Almost all the assignments I submitted for the courses in my master’s in bioethics at the University of Toronto were about Darfur and the people of Darfur. My PhD in bioethics at the University of Birmingham was about them and dedicated to them. And here I am sharing this story with you in the hope that when you come across the next report from a survey conducted during an armed conflict, you would see the people. You would hear the people. You would feel the people—not only those surveyed but also the surveyors. We are all part of a story worth telling. B Healthcare Under Fire (Myanmar) One Exiled Doctor I used to work as a medical doctor in a less developed state than many big cities...
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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 teacher head, 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".