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Record W2061941013 · doi:10.1097/pts.0b013e318283f56d

Exploring Physician Hand Hygiene Practices and Perceptions in 2 Community-Based Canadian Hospitals

2013· article· en· W2061941013 on OpenAlexaffabout
Mackenzie T. Budimir-Hussey, Lucas Ciprietti, Fawad Ahmed, Christopher L. Tarola, Andrea Y. Lo, Maher M. El‐Masri

Bibliographic record

VenueJournal of Patient Safety · 2013
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsUniversity of WindsorWestern University
Fundersnot available
KeywordsHygieneMedicineOdds ratioFamily medicineLogistic regressionDescriptive statisticsConfidence intervalHand washingHealth careNursingInternal medicine

Abstract

fetched live from OpenAlex

OBJECTIVE: The purpose of this study was to explore the self-reported hand hygiene practices and the predictors of hand hygiene among physicians in a midsize Canadian city. METHODS: A descriptive cross-sectional survey using self-report questionnaire administered to a complete list of 354 local physicians. Perception of proper compliance was defined in a participant if he/she indicated performance of hand hygiene before and after every patient contact at least 80% of the time. RESULTS: One hundred fifty-four physicians completed the questionnaire, yielding a 44.9% response rate. Only 45.3% of our sample reported performing preprocedure and postprocedure hand hygiene at least 80% of the time. Stepwise logistic regression results suggested that the variables "presence of hand hygiene auditing" (odds ratio [OR], 3.2; 95% confidence interval [CI], 1.47-6.91), "being too busy" (OR, 0.43; 95% CI, 0.20-0.90), "forgetfulness" (OR, 0.27; 95%, CI, 0.13-0.56), and "the perception that hand hygiene products are damaging to the skin" (OR, 0.31;95% CI, 0.11-0.88) were the only independent predictors of physician hand hygiene compliance. CONCLUSIONS: Hand hygiene compliance among physicians remains an issue. The findings emphasize the need of health-care institutions to prioritize hand hygiene by ensuring proper promotion and enforcement of current policies to all practicing HCPs.

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 score0.078
Threshold uncertainty score0.983

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 communication0.0000.001
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 head0.064
GPT teacher head0.317
Teacher spread0.254 · 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

Citations6
Published2013
Admission routes2
Has abstractyes

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