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Record W2910476711 · doi:10.15173/sciential.v1i1.1921

How Our Healthcare System Failed During the SARS Outbreak

2018· article· en· W2910476711 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
venuePublished in a venue whose home country is Canada.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueSciential - McMaster Undergraduate Science Journal · 2018
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsMcMaster University
Fundersnot available
KeywordsOutbreakHealth careTollPandemicDeath tollMedicineHealth professionalsMedical emergencyHealthcare systemAction (physics)Infectious disease (medical specialty)Coronavirus disease 2019 (COVID-19)DiseaseVirologyEnvironmental healthEconomic growthImmunologyPathology

Abstract

fetched live from OpenAlex

Severe Acute Respiratory Syndrome (SARS) was an active pandemic in the spring of 2003, ravaging places such as Hong Kong and Canada. In Ontario, the healthcare system was extremely unprepared, hence resulting in a multitude of deaths, in which many were healthcare professionals. In contrast, Vancouver took the necessary precautions leading up to the outbreak, and the benefits of this can be seen in their low death toll. In the future, the Ontario healthcare system needs to learn from these mistakes by preparing personal protective equipment and educating healthcare professionals on proper infectious disease control protocol. This is a call to action for the Ontario healthcare system.

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.

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)0.006
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.383
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0120.001
Scholarly communication0.0010.002
Open science0.0020.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.001

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 head0.048
GPT teacher head0.374
Teacher spread0.326 · 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