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Record W4387978084 · doi:10.1353/nib.0.a911244

Healthcare Under Fire (Myanmar)

2023· article· en· W4387978084 on OpenAlexaboutno aff
One Exiled Doctor

Bibliographic record

VenueNarrative Inquiry in Bioethics · 2023
Typearticle
Languageen
FieldHealth Professions
TopicHealth and Conflict Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMandateHealth careTask (project management)PaymentPsychologyPublic relationsSocial psychologyMedical emergencyBusinessMedicineLawPolitical scienceManagement

Abstract

fetched live from OpenAlex

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0210.004
Scholarly communication0.0040.005
Open science0.0020.009
Research integrity0.0040.013
Insufficient payload (model declined to judge)0.0100.001

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.

Opus teacher head0.451
GPT teacher head0.568
Teacher spread0.117 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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