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Record W3189839085 · doi:10.5430/jnep.v13n8p1

Immunization training modules: Identifying student nurse learning

2023· article· en· W3189839085 on OpenAlexvenueno aff
Michael S. Robinson, Katie Bates, Karlen E. Luthy, Janelle L. B. Macintosh, Renea L. Beckstrand

Bibliographic record

VenueJournal of Nursing Education and Practice · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsNursingVaccinationNurse educationMedicineImmunizationPsychologyMedical educationFamily medicineImmunology

Abstract

fetched live from OpenAlex

Background and objective: Despite the importance of vaccination in disease prevention some people choose to remain unvaccinated. Nurses are influential in the choice to vaccinate. Considering the possibility of poor public understanding of vaccines and need for continued improvement in vaccination rates, it is essential for nurses to be knowledgeable and adept at addressing vaccine concerns, especially since the COVID-19 pandemic. Vaccination education formally begins in nursing school. The objective of this study was to identify nursing students’ vaccine understanding by exploring information learned from formal online vaccine education specifically the Nursing Initiative Promoting Immunization Training Modules (NIP-IT).Methods: Nursing students enrolled in a Community Health Nursing course were required to complete three online, self-study, modules entitled Vaccine Preventable Diseases, Vaccine Concerns, and Nursing Roles. The nursing students who completed these modules responded, in writing, to an open-ended prompt asking them to identify what new piece of information they learned. Responses gathered from 244 nursing students between September of 2016 and April of 2018 were categorized and grouped according to theme using a first and second cycle coding process. Responses containing more than one idea were considered separate responses and categorized accordingly totaling 273 responses.Results: Nursing student responses revealed five major themes regarding new information learned from the online modules: (1) barriers to vaccination; (2) components of vaccines; (3) the influence of nurses; (4) vaccine-preventable diseases; and (5) community immunity.Conclusions: Formal vaccine education is a critical component of a comprehensive nursing program. The nursing students in this study described information they learned when completing the NIP-IT modules, thus it was inferred the nursing students did not have a full understanding of vaccine concepts prior to viewing the modules. Formal nursing school vaccine education is essential in developing nurses capable of navigating vaccine issues and promoting health and preventing disease through vaccination advocacy.

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)0.002
metaresearch head score (Gemma)0.011
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 score0.003
Threshold uncertainty score0.012

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.144
GPT teacher head0.493
Teacher spread0.349 · 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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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