Relationship Between Vaccine Knowledge and Attitudes of Undergraduate Nursing Students
Bibliographic record
Abstract
Background: Vaccine hesitancy is a growing threat to public health worldwide. While COVID-19 vaccinations and vaccine hesitancy have dominated headlines recently, routine immunizations will continue to be an important focus both during and after the COVID-19 pandemic. Nursing students represent the next generation of immunizers and health promoters; however, the vaccine knowledge and attitudes of this population are largely unknown. Purpose: The purpose of this study was to assess baccalaureate nursing students’ knowledge of, and attitudes toward, routine vaccinations. Students’ self-reported vaccination influences were also examined. Methodology: A quantitative descriptive research design was utilized along with correlational methodology. Vaccine knowledge and vaccine acceptance were measured using pre-existing instruments with satisfactory psychometric properties. A single question on vaccine influences was composed by the authors based on common vaccine information sources referred to in scholarly literature. Sample and Setting: The sample consisted of 145 fourth-year nursing students at a Southwestern Ontario university who completed an in-class, online survey. Data collection occurred in February 2020, prior to the widespread impacts of the COVID-19 pandemic. Results: The participants were found to have high mean vaccine knowledge scores (7.8/9, SD ± 1.5), high vaccine acceptance scores (123.3/140, SD ± 16.1), and the two variables were positively correlated (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. The participants also demonstrated considerable uncertainty about the presence and safety of mercury in vaccines. Nearly half of the participants (46.2%; n = 67) considered nursing school to be the leading vaccination influence in their lives, followed by healthcare providers (29.7%; n = 43). Conclusions: Nursing educators are in a prime position to positively impact students’ vaccine knowledge and attitudes and should consider targeting their curriculum to dispelling 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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.004 | 0.000 |
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.
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".