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Record W2077525199 · doi:10.12927/hcq.2015.24119

Response to a Serious Flood: The St Joseph’s Healthcare Experience

2014· article· en· W2077525199 on OpenAlexaff
Tina Dhanoa, Hugh D. Fuller, Bryan Herechuk, Stephanie Trowbridge, Victoria Raab, Anne-Marie MacDonald, Tara Coffin Simpson, Carolyn Gosse

Bibliographic record

VenueHealthcare Quarterly · 2014
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsSt. Joseph’s Healthcare Hamilton
Fundersnot available
KeywordsFlood mythHealth careMedical emergencyEmergency departmentBest practiceOperations managementPsychologyBusinessNursingMedicinePublic relationsManagementPolitical scienceEngineeringHistoryEconomicsLaw

Abstract

fetched live from OpenAlex

Water spread like liquid fire damaging more than 60,000 sq. ft. of clinical and support space, bringing the emergency department (ED) and operating rooms at St Joe's to an abrupt halt. Staff mobilized immediately, calling a hospital-wide Code Aqua (flood) and Code Green (evacuation) for the ED, and launching into action to save equipment and supplies worth millions of dollars. Our path to recovery has been difficult, but we have emerged stronger as an organization. The urgent necessity of rethinking care led to radical innovation, particularly in the flow and care of patients admitted through the ED.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.768
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.003

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 head0.045
GPT teacher head0.408
Teacher spread0.363 · 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 teacher head, not a consensus.

Study designNot applicable
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

Citations1
Published2014
Admission routes1
Has abstractyes

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