Vaccine Knowledge and Vaccine Attitudes of Undergraduate Nursing Students
Bibliographic record
Abstract
Vaccine hesitancy is a growing threat to public health worldwide; however, the vaccine knowledge and attitudes of nursing students—a population of future immunizers and health promoters—are largely unknown. The purpose of this descriptive research study was to assess baccalaureate nursing students’ knowledge and acceptance of vaccinations as well as leading, self-reported vaccination influences in their lives. The sample consisted of 145 fourth-year nursing students at a Southwestern Ontario university who completed an in-class, online survey in February 2020 (pre-COVID-19 restrictions) consisting of the Vaccination Knowledge Scale, the Vaccine Acceptance Instrument, and demographic and vaccination influence questions. The participants were found to have high mean vaccine knowledge scores (7.8/9, SD ± 1.5) and vaccine acceptance scores (123.3/140, SD ± 16.1), and the two variables were positively correlated using Pearson’s correlation (r[143] = .69, p < .001). However, the vaccine acceptance results revealed varying degrees of vaccine hesitancy, and the students displayed the lowest scores in the subscale pertaining to the role of government in requiring vaccinations. Nursing school was selected as the leading vaccine influence among the participants, but healthcare providers were chosen as a primary influence by students with lower vaccine knowledge scores. Nursing educators are in a prime position to positively impact students’ knowledge of and attitudes toward vaccination and should consider providing targeted education toward common vaccination misconceptions among nursing students.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".