Notice bibliographique
Résumé
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...
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,001 | 0,003 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,002 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».