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Record W1984732705 · doi:10.1016/j.ajic.2009.10.002

The pandemic influenza planning process in Ontario acute care hospitals

2009· article· en· W1984732705 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.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueAmerican Journal of Infection Control · 2009
Typearticle
Languageen
FieldHealth Professions
TopicDisaster Response and Management
Canadian institutionsPublic Health OntarioUniversity of TorontoQueen's UniversityKingston General Hospital
FundersQueen's University
KeywordsPandemicMedicineInfluenza pandemicPreparednessHuman mortality from H5N1Acute careMedical emergencyHealth careCoronavirus disease 2019 (COVID-19)Quarter (Canadian coin)Family medicineInfectious disease (medical specialty)DiseaseEconomic growthInternal medicineGeography

Abstract

fetched live from OpenAlex

BackgroundThere will be little time to prepare when an influenza pandemic strikes; hospitals need to develop and test pandemic influenza plans beforehand.MethodsAcute care hospitals in Ontario were surveyed regarding their pandemic influenza preparedness plans.ResultsThe response rate was 78.5%, and 95 of 121 hospitals participated. Three quarters (76.8%, 73 of 95) of hospitals had pandemic influenza plans. Only 16.4% (12 of 73) of hospitals with plans had tested them. Larger (χ2 = 6.7, P = .01) and urban hospitals (χ2 = 5.0, P = .03) were more likely to have tested their plans. 70.4% (50 of 71) Of respondents thought the pandemic influenza planning process was not adequately funded. No respondents were “very satisfied” with the completeness of their hospital's pandemic plan, and only 18.3% were “satisfied.”ConclusionImportant challenges were identified in pandemic planning: one quarter of hospitals did not have a plan, few plans were tested, key players were not involved, plans were frequently incomplete, funding was inadequate, and small and rural hospitals were especially disadvantaged. If these problems are not addressed, the result may be increased morbidity and mortality when a virulent influenza pandemic hits. There will be little time to prepare when an influenza pandemic strikes; hospitals need to develop and test pandemic influenza plans beforehand. Acute care hospitals in Ontario were surveyed regarding their pandemic influenza preparedness plans. The response rate was 78.5%, and 95 of 121 hospitals participated. Three quarters (76.8%, 73 of 95) of hospitals had pandemic influenza plans. Only 16.4% (12 of 73) of hospitals with plans had tested them. Larger (χ2 = 6.7, P = .01) and urban hospitals (χ2 = 5.0, P = .03) were more likely to have tested their plans. 70.4% (50 of 71) Of respondents thought the pandemic influenza planning process was not adequately funded. No respondents were “very satisfied” with the completeness of their hospital's pandemic plan, and only 18.3% were “satisfied.” Important challenges were identified in pandemic planning: one quarter of hospitals did not have a plan, few plans were tested, key players were not involved, plans were frequently incomplete, funding was inadequate, and small and rural hospitals were especially disadvantaged. If these problems are not addressed, the result may be increased morbidity and mortality when a virulent influenza pandemic hits.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.051
Threshold uncertainty score0.436

Codex and Gemma teacher scores by category

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

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 head0.021
GPT teacher head0.401
Teacher spread0.380 · 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