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Enregistrement W2753278150 · doi:10.1213/ane.0000000000002340

Obstetrics and Gynecology in Low-Resource Settings: A Practical Guide

2017· article· en· W2753278150 sur OpenAlexaffabout
Ronald B. George

Notice bibliographique

RevueAnesthesia & Analgesia · 2017
Typearticle
Langueen
DomaineMedicine
ThématiqueGlobal Health and Surgery
Établissements canadiensIzaak Walton Killam Health Centre
Organismes subventionnairesnon disponible
Mots-clésMedicineObstetrics and gynaecologyOutreachHealth careResource (disambiguation)Medical educationNursingFamily medicinePregnancyEconomic growth

Résumé

récupéré en direct d'OpenAlex

Dissemination of high-quality medical knowledge to areas of low resources is critical to the care of millions of people. Obstetrics and Gynecology in Low-Resource Settings: A Practical Guide delivers on its goal to provide evidence-informed practical guidance to care providers working in low-resource countries. The editor of the book, Dr Nawal M. Nour, gives readers practical management strategies for problems faced in low-income countries: human immunodeficiency virus–complicated pregnancy, obstetric fistula, unsafe abortions, violence against women, and anesthesia-related morbidity. These topics are important to an anesthesiologist who plans to work in a low-resource environment. Dr Nour is director of the Division of Global Obstetrics and Gynecological Health at Brigham and Women’s Hospital in Boston, Mass, and associate professor at Harvard Medical School. She established the African Women’s Health Center, which focuses on care for women who have undergone female genital mutilation. The African Women’s Health Center also provides outreach programs to the African community in Boston. Nour wrote this book to “present an epidemiologic picture of their subject and practical approaches to treating real-world patients.” Dr Nour lends her expertise to chapter 11, “Female Genital Cutting.” I have been an active participant in a number of global health efforts to improve anesthesia conditions and to help organize a meeting to prepare anesthesiologists working and educating in low-resource environments. For this reason, I had a strong appreciation for the sections written by Nour: “Are You Doing This for the Right Reasons?”; “Is Global Health Right for You?”; and “How Do You Fit Into the Picture of Global Women’s Health?” These sections enticed me to reflect on my own motivations; as Nour says, “improving the health and well-being of women worldwide is worth the effort.” The chapter titled “Maternal Mortality in Low-Resource Countries” was another highlight for me. Lalonde and Miller provide readers with a well-indexed history of the struggle to reduce maternal mortality worldwide. They discuss the unsurprising major causes of maternal mortality and practical considerations, but they also provide readers with observations on how we may continue to reduce the direct causes of maternal mortality. Part IV of the book comprises 2 chapters focusing on the teamwork challenges of obstetric and gynecology practice in low-resource environments. In Chapter 13, Dinesh Jagannathan and Bhavani Kodili describe a detailed picture of what it is like to provide anesthesia in these environments and the importance of safe anesthesia to reduce maternal and newborn mortality. They emphasize the need for essential equipment, drugs, protocols, and, perhaps most important, well-trained anesthesia providers. This chapter illustrates the benefits of educational initiatives developed by nonprofit organizations to enhance the safety of anesthesia for childbirth. In Croatia, the nonprofit organization Kybele provided a program of education for cesarean delivery anesthesia that increased the use of regional anesthesia from 18% to 59%.1 It also highlights the anesthesia fellowship training programs from the World Federation of Societies of Anaesthesiologists and their web-based educational programs.2 The chapter on anesthesia for childbirth provides a necessary summary of the limitations of monitoring, medication, transfusion medicine, and anesthesia equipment that affect clinicians in low-resource environments. Jagannathan and Kodili also provide readers with practical protocols for the management of women’s care during childbirth. These include protocols for neuraxial and general anesthesia for cesarean delivery, as well as postoperative pain management. They adapted a World Health Organization reference table (Table 13.1) to describe the minimum requirements for the provision of obstetric anesthesia. There is also a concise section on labor analgesia. Neuraxial labor analgesic techniques are used infrequently in low-resource environments, and are often overlooked in many texts regarding anesthesia care in low-resource environments. Jagannathan and Kodili briefly mention nonneuraxial aspects of labor analgesia, including parenteral opioids and nitrous oxide. My personal experience might suggest a need to expand on labor analgesia in future editions. As an obstetric anesthesiologist who has worked alongside local clinicians in many low-resource environments, I would recommend this book to colleagues and students who have worked or are planning to work in these challenging environments. I believe this book has value in providing greater insight to anesthesiologists working as part of a global health mission that targets maternal and newborn health. We are rarely involved with isolated obstetric anesthesia projects in low-resource environments. It is important for us to recognize some of the problems faced by our obstetrician teammates to improve how we work alongside one another. In the current era, I hope a digital version will be available for easy access and transportability. The practical aspects of this book will be of value to friends working in low-resource environments. My own copy is now packed in my travel bag, both for review during a long flight and to pass forward to a global health colleague on my next trip abroad. Ronald B. George, MD, FRCPCDepartment of Women’s & Obstetric AnesthesiaIWK Health CentreHalifax, Nova Scotia, Canada[email protected]

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 machine sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,007
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Sans objet · Signal consensuel: Sans objet
GenreSignal candidat: Autre · Signal consensuel: aucune
Score de désaccord entre enseignants0,038
Score d'incertitude au seuil0,128

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0020,007
Méta-épidémiologie (sens strict)0,0020,001
Méta-épidémiologie (sens large)0,0010,001
Bibliométrie0,0030,001
Études des sciences et des technologies0,0010,001
Communication savante0,0030,005
Science ouverte0,0020,003
Intégrité de la recherche0,0040,008
Charge utile insuffisante (le modèle a refusé de juger)0,0380,037

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.

Tête enseignante Opus0,018
Tête enseignante GPT0,321
Écart entre enseignants0,302 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_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 découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.

Les modèles n’ont appliqué aucune catégorie : rien dans la taxonomie ne correspondait à ce travail.
Devis d'étudeSans objet
Domainenon disponible
GenreAutre

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations1
Publié2017
Routes d'admission2
Résumé présentoui

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