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Record W2417393493

Access to hand hygiene in eastern Ontario.

2009· article· en· W2417393493 on OpenAlexaffabout
Joseph Vayalumkal, Colette Ouellet, Virginia Roth

Bibliographic record

VenuePubMed · 2009
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsPublic Health Agency of Canada
Fundersnot available
KeywordsMedicineMedical emergencyHygieneIntensive careLong-term careInfection controlHealth careNursingIntensive care medicine
DOInot available

Abstract

fetched live from OpenAlex

BACKGROUND: Hand hygiene compliance improves when alcohol-based hand products (ABHP) are provided at the point-of-care (POC). However, it is not known how many facilities have the infrastructure available to provide easy access to ABHP currently. OBJECTIVES: To describe the extent to which facilities in the Champlain Infection Control Network (CICN) provide POC access to ABHP. METHODS: A survey was conducted of all healthcare facilities in the CICN in October 2007. Sites were asked to complete a one-page questionnaire regarding number and location of ABHP dispensers on one ward in their facilities. The primary outcome measures included: the proportion of facilities providing any POC access to ABHP and the proportion of ABHP dispensers that were at POC, hallways and other areas. RESULTS: A total of 18 of 59 (31%) long-term care facilities (LTCF) and 14 of 18 (78%) acute-care facilities (ACF) participated in the survey. Intensive care units (ICUs) were present in seven (50%) of the ACF. POC access to ABHP was provided in 44% of LTCF, 50% of ACF and 71% of ICUs surveyed. In LTCF 20% of ABHP dispensers were at the POC compared to 23% in ACF and 42% in ICUs. CONCLUSIONS: Although ABHP is available in these settings, most dispensers are not provided at the POC. Hospitals and LTCF need to increase the number of ABHP dispensers available, with a particular emphasis on placing them at the POC in accordance with provincial guidelines.

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 score0.378
Threshold uncertainty score0.996

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 head0.064
GPT teacher head0.319
Teacher spread0.256 · 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

Citations4
Published2009
Admission routes2
Has abstractyes

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