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Record W2159094130 · doi:10.1177/1054773811409032

Predicting Seasonal Influenza Vaccination Among Hospital-Based Nurses

2011· article· en· W2159094130 on OpenAlex
Theresa Marentette, Maher M. El‐Masri

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.

Bibliographic record

VenueClinical Nursing Research · 2011
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsVaccinationMedicineHealth careSeasonal influenzaCross-sectional studyFamily medicineDescriptive statisticsCoronavirus disease 2019 (COVID-19)ImmunologyInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

A descriptive cross-sectional online survey of a convenience sample of 202 hospital-based nurses was conducted to explore the factors associated with influenza vaccination. The findings suggest that the independent predictors of influenza vaccination were perception of job as a risk increasing factor (OR = 12.14; 95% CI [1.89, 78.08]), workplace vaccination clinics and campaigns (OR = 2.88; 95% CI [1.12, 7.38]), vaccination in the previous season (OR = 34.80; 95% CI [12.99, 93.28]), viewing vaccination as an inconvenience (OR = 0.22; 95% CI [0.07, 0.67]), and one's belief that the immune system provides better protection than the vaccine (OR = 0.29; 95% CI [0.11, 0.77]). In conclusion, the findings support the existing literature with regards to low vaccination rates among health care providers. Furthermore, the identification of the predictors of influenza vaccination among nurses may assist administrators and policy makers with the implementation of evidence-based vaccination strategies.

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)0.006
metaresearch head score (Gemma)0.017
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.074
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.017
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.002
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 head0.322
GPT teacher head0.551
Teacher spread0.230 · 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