Factors influencing Australian nursing and midwifery students COVID-19 vaccination intentions
Bibliographic record
Abstract
Background: Vaccination for COVID-19 has become a cornerstone management plan for many countries. Australian state governments made vaccinations mandatory for all healthcare workers. Despite evidence on the important role vaccines hold in preventing or decreasing serious disease, there have been many nurses and midwives who have demonstrated vaccine hesitancy. This hesitancy has also been present in undergraduate nursing and midwifery students. The aim of this study was to explore factors influencing Australian nursing and midwifery students' intentions towards receiving the COVID-19 vaccine; identify the barriers and facilitators to obtaining the COVID-19 vaccine; and understand students' perceptions of mandating the COVID-19 vaccine and identify any impact on their studies.. Methods: Cross-sectional mixed method study utilising an online survey platform. Data were analysed using binomial and multinomial logistic regression through Statistical Package for the Social Sciences. A content analysis was completed for the qualitative data. Results: = 409), 133 participants were midwives and 30 were in dual nursing/midwifery programs. Education and communication were identified as two major factors that facilitate vaccine acceptance. Conclusions: Vaccines are integral in the prevention of contracting COVID-19 or reducing the severity of the symptoms. However, many nursing and midwifery students have shown reluctance towards getting vaccinated. The mandate to be vaccinated to attend clinical placement has led to the inability of some students to complete their course. The findings from this study are valuable in informing the future COVID-19 vaccination strategies and improving vaccine acceptance. COVID-19 remains a global health risk and therefore further research is needed of vaccine acceptance amongst the future health workforces. It is crucial knowledge for policy makers and healthcare services as they plan for any future pandemics and implement Australia's national vaccine strategy.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".