Cervical cancer prevention by vaccination: nurses’ knowledge, attitudes and intentions
Bibliographic record
Abstract
AIM: This paper is a report of a survey: (1) to document nurses' knowledge, attitudes and information needs regarding human papillomavirus prevention and (2) to determine factors associated with their willingness to recommend human papillomavirus vaccines. BACKGROUND: Persistent infection with human papillomavirus has been causally linked to cervical cancer. Two human papillomavirus vaccines have recently been approved for use in more than 65 countries. Nurses' level of support for the prevention of human papillomavirus related diseases by vaccination has not been researched. METHODS: A survey was conducted in 2007. Self-administered questionnaires were mailed to 1799 randomly selected nurses. Descriptive statistics were generated for all variables. Multivariable logistic regression models were estimated to determine variables associated with the willingness to recommend human papillomavirus vaccines. RESULTS: A total of 946 questionnaires were analyzed and showed that: 97% of nurses perceived routinely recommended vaccines as very useful; 93% would support human papillomavirus vaccination if it is publicly funded; 85% would recommend human papillomavirus vaccines to their patients; 33%, 46% and 61% expect the vaccination to permit screening to begin later in life, reduction of the frequency of screening, and reduction of the number of postscreening interventions, respectively. Respondents' knowledge score was 3.8 out of 7. Several modifiable factors, including knowledge, perceived self-efficacy, and societal and colleagues support were associated with willingness to recommend vaccines. CONCLUSION: Most nurses' support human papillomavirus vaccination, but their active involvement should not be taken for granted. Targeted educational efforts are needed to ensure nurses' involvement in the prevention of human papillomavirus-related diseases.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".