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Record W2113930173 · doi:10.1186/1753-6561-5-s6-p265

Survet of infection control measures and design of emergeny rooms in Quebec, Canada: an overview of the actual situation

2011· article· en· W2113930173 on OpenAlexaffabout
A-M Lowe, P Dolcé, B Baril, C Sauvé, D. -O. Goulet, Anne Fortin

Bibliographic record

VenueBMC Proceedings · 2011
Typearticle
Languageen
FieldMedicine
TopicEmergency and Acute Care Studies
Canadian institutionsCentre Hospitalier Universitaire Sainte-JustineHôpital Charles-Le MoyneInstitut National de Santé Publique du Québec
Fundersnot available
KeywordsOvercrowdingMedicineInfection controlControl (management)Medical emergencySurgeryComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

An electronic survey covering design, hand hygiene, IC measures and housekeeping of ER was done using Survey Monkey software. The questionnaire was sent in September 2010 to IC practitioners of Quebec acute-care settings with >1000 admissions/year. Data were analyzed with Epi-Info 3.5.2. The survey was completed by 63/89 (71%) hospitals. ER had a mean of 22 beds (range: 5-52), including 30,3% of single rooms. Airborne isolation rooms (AIR) were present in 87% of ER (range: 0-8 AIR). The ratio of ER that had a proportion of toilet/bed between 0 to 40% was 85%. Hand hygiene stations were located next to 78,5% of beds. Audits on hand hygiene compliance were performed in 35/63 (55,5%) of ER within the past two years. The compliance rate was <50% in 90,6% of ER. A designated area in the waiting room to cohort patients presenting with infectious diseases symptoms was present in 87,1% ER. Surveillance of MRSA and VRE were done in 90,4% of ER. An IC committee specific to ER was implemented in only 4,8% of ER. Dedicated housekeeping personnel were present in 76,2% of ER. In Québec’s ER, only 30% of beds were designed as single rooms and compliance to hand hygiene was low. More evidence-based data and guidelines are needed on IC in ER.

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)0.000
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 score0.177
Threshold uncertainty score0.418

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.103
GPT teacher head0.287
Teacher spread0.185 · 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
Published2011
Admission routes2
Has abstractyes

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