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Record W2905859223

Looking Back on a Post-Disaster Response: Analyzing Medical Activities to Understand the Transformation of Public Buildings to Medical Facilities

2012· article· en· W2905859223 on OpenAlexaboutno aff
KM Keddy, Ahsen Özsoy, Ozge Atalay

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsnot available
Fundersnot available
KeywordsDisaster responsePublic relationsPublic healthArchitectural engineeringBusinessEnvironmental planningEnvironmental healthPolitical scienceEmergency managementEngineeringEconomic growthGeographyMedicineNursingEconomics
DOInot available

Abstract

fetched live from OpenAlex

The Halifax Explosion in December of 1917 in Nova Scotia, Canada resulted when a munitions ship collided with another ship in the Halifax Harbor during World War I. This resulted in approximately 12,000 damaged buildings, 6,000 people left homeless, approximately 1,600 people dead, and close to 9,000 people injured (Kitz 1989: 23). This was the largest human-caused explosion prior to the atomic bombs dropped on Hiroshima and Nagasaki in Japan during WWII, causing a tsunami in the Halifax Harbour as well as total destruction for miles.Numerous perspectives on the Explosion exist including historical accounts, literary expressions, scientific studies, and relief responses. However, the medical aspect of the disaster has not been analyzed in a way that provides a reconstruction of the post-disaster built environment in Halifax that emerged to handle the injured and the remains of the dead. Numerous public buildings were quickly transformed into medical facilities such as dressing stations, depots for medical supplies, eye surgery clinics, emergency hospitals, and a city morgue.This research is an analysis of the descriptions of the post-disaster behavioral activities of the medical personnel to help construct an accurate representation of the post-disaster built environment. My argument is that by looking at the documented human behavior and activities of the medical personnel, the socio-spatial characteristics of the newly transformed medical buildings will be reflected in much the same way that the opposite approach of behavioral plan analysis works to reveal behavioral implications.My research questions include, what architectural characteristics of the buildings enabled the transformation from one building type to a medical facility building type? How well did these buildings accommodate the new programmatic requirements? Careful review of the activities and physical setting descriptions found in personal narratives, letters, newspaper articles, committee notes, and pension claims documents found in the provincial archives have revealed how people managed and experienced the immediate medical response required and the physical settings that accommodated these activities.Halifax had preparedness unlike other disasters because it happened during World War I, it was a major military port, and at that time in Halifax, there were numerous trained medical personnel, military and convalescent hospitals, as well as many war-time activities done by volunteers and an infrastructure already in place for returning war veterans. This interpretive-historical study contributes new insights to the existing perceptions of the post-disaster phase of the Halifax Explosion. It also contributes to the research on ‘disaster healthcare’ illustrating how types of disaster readiness and the transformation of the built environment can contribute to a reduction in medical complications and deaths.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.001
metaresearch head score (Gemma)0.002
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: Empirical
Teacher disagreement score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.003
Science and technology studies0.0030.006
Scholarly communication0.0040.004
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.090
GPT teacher head0.401
Teacher spread0.312 · 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
Published2012
Admission routes1
Has abstractyes

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