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Record W2146584149 · doi:10.1186/s13012-015-0278-1

Improving patient safety by optimizing the use of nursing human resources

2015· article· en· W2146584149 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueImplementation Science · 2015
Typearticle
Languageen
FieldHealth Professions
TopicPatient Safety and Medication Errors
Canadian institutionsMcGill UniversityMcGill University Health CentreUniversité de Sherbrooke
FundersCanadian Institutes of Health Research
KeywordsMedicineStaffingAdverse effectEmergency medicinePatient safetyIncidence (geometry)Health careHealth administrationIntensive care medicineMedical emergencyNursingPublic healthInternal medicine

Abstract

fetched live from OpenAlex

BACKGROUND: Recent ecological studies have suggested that inadequate nurse staffing may contribute to the incidence of adverse events in acute care hospitals. However, longitudinal studies are needed to further examine these associations and to identify the staffing patterns that are of greatest risk. The aims of this study are to determine if (a) nurse staffing levels are associated with an increased risk of adverse events, (b) the risk of adverse events in relationship to nurse staffing levels is modified by the complexity of patient requirements, and (c) optimal nurse staffing levels can be established. METHODS/DESIGN: A dynamic cohort of all adult medical, surgical, and intensive care unit patients admitted between 2010 and 2015 to a Canadian academic health center will be followed during the inpatient and 7-day post-discharge period to assess the occurrence and frequency of adverse events in relationship to antecedent nurse staffing levels. Four potentially preventable adverse events will be measured: (a) hospital-acquired pneumonia, (b) ventilator-associated pneumonia, (c) venous thromboembolism, and (d) in-hospital fall. These events were selected for their high incidence, morbidity and mortality rates, and because they are hypothesized to be related to nurse staffing levels. Adverse events will be ascertained from electronic health record data using validated automated detection algorithms. Patient exposure to nurse staffing will be measured on every shift of the hospitalization using electronic payroll records. To examine the association between nurse staffing levels and the risk of adverse events, four Cox proportional hazards regression models will be used (one for each adverse event), while adjusting for patient characteristics and risk factors of adverse event occurrence. To determine if the association between nurse staffing levels and the occurrence of adverse events is modified by the complexity of patient requirements, interaction terms will be included in the regression models, and their significance assessed. To assess for the presence of optimal nurse staffing levels, flexible nonlinear spline functions will be fitted. DISCUSSION: This study will likely generate evidence-based information that will assist managers in making the most effective use of scarce nursing resources and in identifying staffing patterns that minimize the risk of adverse events.

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.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.285
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
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 head0.416
GPT teacher head0.558
Teacher spread0.142 · 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