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Record W3119168918 · doi:10.5430/jnep.v11n5p24

Evaluation of a quality improvement program to prevent healthcare acquired infections in an acute care hospital

2021· article· en· W3119168918 on OpenAlexafffundvenue
Laurence Bernard, Alain Biron, Anaïck Briand, Samy Taha, Mélanie Lavoie‐Tremblay

Bibliographic record

VenueJournal of Nursing Education and Practice · 2021
Typearticle
Languageen
FieldMedicine
TopicInfection Control in Healthcare
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversité de Montréal
FundersMcGill University
KeywordsAuditDocumentationInfection controlHealth careMedicineNursingThematic analysisQuality managementPatient safetyHealth professionalsTeamworkQuality (philosophy)Medical emergencyBusinessIntensive care medicineQualitative research

Abstract

fetched live from OpenAlex

Objective: The general purpose of the study was to evaluate a specific prevention program and its effects on infection prevention practices as part of continuous improvements in patient safety. Infection prevention is a global priority aimed at reducing mortality and morbidity rates related to infections acquired while under care.Methods: A descriptive study was carried out through a documentation analysis and semi-structured interviews with 13 healthcare professionals working in a healthcare centre where the infection prevention program was developed and implemented.Results: The thematic analysis identified three major axes: perceptions concerning audits and huddles strategies, the positive effects of the program on team building and, finally, its sustainability and continuous improvement.Conclusions: Globally, program enhanced the habits of professionals by developing an accurate perception of infections and the way to manage the related risk. The program Controlling Specific Infections Successful Strategies (CSISS) is seen as effective and sustainable by the participants. It contributes to a collaborative safety culture to reduce nosocomial infection rates.

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)0.002
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.951
Threshold uncertainty score0.692

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
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 head0.128
GPT teacher head0.563
Teacher spread0.435 · 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 designOther design
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

Citations3
Published2021
Admission routes3
Has abstractyes

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