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Enregistrement W956440973 · doi:10.1891/1062-8061.23.11

Disasters, Nursing, and Community Responses: A Historical Perspective

2014· article· en· W956440973 sur OpenAlexaboutno aff
Barbra Mann Wall

Notice bibliographique

RevueNursing History Review · 2014
Typearticle
Langueen
DomaineHealth Professions
ThématiqueDisaster Response and Management
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésNatural disasterTriageDisaster researchSociologyPerspective (graphical)Emergency managementPublic relationsWork (physics)PsychologyCriminologyPolitical scienceMedicineMedical emergencyLawGeographyEngineeringComputer science

Résumé

récupéré en direct d'OpenAlex

Modern disaster planning has taken on increased importance and urgency in light of recent dramatic increase in natural and man-made that resulted in enormous human and economic losses.1 Such planning is aided by examining historical role of nurses in disaster responses. Nurses occupy vital positions in disaster care because of their unique roles with pa- tients and their experience in such areas as evacuation, triage, physical and psychological care, screening measures, case finding, vaccinations, monitor- ing, and disease surveillance and prevention.What does history tell us about nurses' roles in disasters, particularly their provision of disaster relief during initial response phase? Why is this important for disaster responses? And how can this knowledge enhance our understandings of notion of phenomena? For this discus- sion, disaster is defined as a social disruption resulting from natural causes such as earthquakes and hurricanes, technological causes such as explosions or nuclear accidents, and conflict situations such as wartime.2 Research on term behavior has been a significant feature of disaster stud- ies in sociology, but it has not been examined from standpoint of history of nursing. Sociologists Thomas Drabek and David McEntire argue that emergent phenomena include the appearance of interorganizational networks after disaster which attempt to fulfill important societal functions made evident by an extreme event. These networks are composed of many organizations that work together to resolve demands placed on their community in times of disaster.3 Drabek and McEntire argue that people become more cohesive and unified during situations of collective stress, and they work together. Emergent groups often have no previous knowledge of each other, and they may perform nonregular tasks. Local communities are particularly important at this time; they are first to help themselves.4 Often, these emergent groups are most effective and quickest to respond after a disaster.5A history of nursing can contribute to theoretical discussions of emergent behavior. By taking into account nurses' rich heritage in disaster responses, we can learn about which groups should be included in any organizational coordination during disasters.6 This article features case studies of work of nurses and some physicians situated within a local response and one involving international aid. The aim is to enhance understanding of social and po- litical forces that informed nurses' actions and tensions and inconsistences that occurred at particular times in particular places.Doing disaster research has its challenges because records can be lost or destroyed. Some sources are available, however, including newspapers, diaries, letters to family members and other personal correspondence, offi- cial histories from organizations, city records, photographs, and oral sources. Problems include memory loss if a letter was written or an oral history obtained some years later. Yet Joseph Scanlon, who wrote about 1917 Halifax, Nova Scotia, ship explosion, found that disasters are so dramatic that many vividly remember what happened even three-quarters of a century earlier.7Another problem is history is recorded? From whose perspective? A gaping hole includes voices of silenced, including minorities, poor, and others excluded from power. This could be because they may lacked means to document personal experiences, or archivists and librar- ians simply did not seek their stories.8 In my own research, I had to doggedly piece together different sources and read between lines of others to get at silenced voice.In 2010, Arlene Keeling and I edited a book on history of nurs- ing in disasters.9 We concluded, based on 13 case studies, that nurses made crucial independent decisions in crisis situations where time was critical to a person's survival. …

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)0,004
score de la tête « metaresearch » (Gemma)0,005
Version: metacan-v3-hybrid-931329e0061cStatut de validation: machine_predicted_unvalidated
Catégories candidatesaucune
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Qualitatif · Signal consensuel: aucune
GenreSignal candidat: Synthèse · Signal consensuel: Synthèse
Score de désaccord entre enseignants0,009
Score d'incertitude au seuil0,058

Scores du classifieur distillé par catégorie (deux têtes)

CatégorieCodexGemma
Métarecherche0,0040,005
Méta-épidémiologie (sens strict)0,0010,000
Méta-épidémiologie (sens large)0,0010,000
Bibliométrie0,0040,004
Études des sciences et des technologies0,0090,018
Communication savante0,0060,013
Science ouverte0,0010,006
Intégrité de la recherche0,0050,007
Charge utile insuffisante (le modèle a refusé de juger)0,0030,000

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 Opus0,143
Tête enseignante GPT0,441
Écart entre enseignants0,298 · 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'étudeQualitatif
Domainenon disponible
GenreSynthèse

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

En bref

Citations8
Publié2014
Routes d'admission1
Résumé présentoui

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