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Record W7074027817

Hospital disaster preparedness in Switzerland over a decade: a national survey

2017· dissertation· en· W7074027817 on OpenAlexaboutno aff

Bibliographic record

VenueIRIS · 2017
Typedissertation
Languageen
FieldMedicine
TopicPrenatal Screening and Diagnostics
Canadian institutionsnot available
Fundersnot available
KeywordsPreparednessDisaster preparednessOccupational safety and healthLegislationEmergency managementPoison controlSuicide preventionQuarter (Canadian coin)
DOInot available

Abstract

fetched live from OpenAlex

Abstract STUDY OBJECTIVE: To provide a comprehensive assessment of Swiss hospital disaster preparedness in 2016 compared to the 2006 data. METHODS: A questionnaire regarding hospital preparedness in 2016 was addressed to all heads responsible for Swiss emergency departments (EDs). The survey was initiated in May 2016 and finalised in December 2016. RESULTS: Of the 107 ED included, 83 (78%) returned the survey. Overall, 76 (92%) hospitals had a plan in case of a massive influx of patients, and 76 (93%) in case of an accident within the hospital itself. There was a lack in preparedness for specific situations: less than a third of hospitals had a specific plan for NRBC+B patients: Nuclear/Radiological (14; 18%), Biological (25; 31%), Chemical (27; 34%), and Burns (15; 49%), and 48 (61%) of EDs had a decontamination area. Furthermore, less than a quarter of hospitals had specific plans for the most vulnerable populations during disasters such as seniors (12; 15%) and children (19; 24%). CONCLUSIONS: The rate of hospitals with a disaster plan has increased since 2006, reached a level of 92%, but the Swiss health care system remains vulnerable to specific threats like NRBC. The lack of national legislation and Federal funds aimed at fostering hospitals’ preparedness to disasters may be the root cause to explain the vulnerability of Swiss hospitals regarding disaster medicine.

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)0.000
metaresearch head score (Gemma)0.003
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 score0.092
Threshold uncertainty score0.735

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
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 integrity0.0000.000
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 head0.031
GPT teacher head0.352
Teacher spread0.321 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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
Published2017
Admission routes1
Has abstractyes

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