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Record W2762421883 · doi:10.1093/pch/19.6.e35-6

6: Patient Volume and Nursing Overtime Increased Risk of Nosocomial Infection in the NICU

2014· article· en· W2762421883 on OpenAlexaffabout
Marc Beltempo, Guy Lacroix, Michèle Cabot, V Beauchesne

Bibliographic record

VenuePaediatrics & Child Health · 2014
Typearticle
Languageen
FieldMedicine
TopicNosocomial Infections in ICU
Canadian institutionsCentre hospitalier universitaire de Québec
Fundersnot available
KeywordsOvertimeMedicineBacteremiaOvercrowdingNeonatal intensive care unitEmergency medicinePediatricsIntensive care medicineAntibiotics

Abstract

fetched live from OpenAlex

Adult studies have shown that outbreaks in nosocomial infections are associated with understaffing and overcrowding. However, no study has assessed the impact of nurse overtime and patient volume in the neonatal intensive care unit (NICU) on neonatal nosocomial bacteremia. The objective of this study was to assess the impact of patient volume and nurse overtime on neonatal nosocomial bacteremia in all infants hospitalised in the NICU. We conducted a retrospective study on all infants (n=7473) admitted in the CHU de Québec NICU (capacity of 51 beds) from April 1, 2008 to March 31, 2013. Administrative data (nursing overtime hours per day, patient census per day) were obtained from the database Logibec, patient information was obtained from Med-Echo and information on neonatal nosocomial bacteremia was obtained from the local infectious disease database TDR. We assessed the association between administrative data and patient outcomes by using logit and probit models. The average patient volume as percentage of capacity during the study period was 98.7±6.5%. The average overtime as percentage of total daily hours of work was 4.0±3.4%. Overtime is positively related to occupancy levels. For every increase of occupation by one patient, there was an increase of 1.65 h of overtime per day (P<0.001). There were a total of 306 events of nosocomial bacteremia during the study period. Coagulase-negative staphylococcus caused 82% of infections. The overall risk of nosocomial bacteremia was 4.2%. The total number of regular worked hours was not associated with a higher risk of infection. Higher overtime (expressed as percentage of total worked hours) was significantly associated with an increased risk of nosocomial bacteremia (P=0.02). Also, days when overtime was >8% of total worked hours, are significantly associated with an increase risk of nosocomial bacteremia (OR 1.51 [95% CI 1.10 to 2.08]; P=0.01). There was a trend between higher patient volume (100% occupancy compared to 90% capacity) and higher risk of nosocomial bacteremia (OR=1.60; P=0.08). In our study, high patient volume and nursing overtime was directly associated with a higher risk of nosocomial bacteremia in the NICU. This suggests that re-organising the medical workforce to better adapt to periods of high activity in the NICU should become an integral part of nosocomial infection prevention.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.008
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.072
Threshold uncertainty score0.144

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.271
Teacher spread0.264 · 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 source (direct Gemma or distilled Codex), 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".

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Citations0
Published2014
Admission routes2
Has abstractyes

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