The Human Papillomavirus vaccine: An oncology nursing issue
Bibliographic record
Abstract
In Canada, approximately 1,500 women are diagnosed with cervical cancer every year, and 581 will die of the disease (WHO/ICO Information Centre on HPV and Cervical Cancer, 2007). The importance of preventing cervical cancer is clear, as the effects that this disease has on the lives of women and their families regardless of culture, sex, nationality or country is evident. With the recent media attention and release of the Human Papillomavirus (HPV) vaccine in Canada, it is crucial that oncology nurses understand HPV, its role in the development of cervical cancer, and the HPV vaccine. A brief overview of HPV and its involvement in the development of cervical cancer will be discussed in this paper. In addition, information on the HPV vaccine and its implications, as well as the current policy for the vaccine in Canada will be addressed. It will become evident how the role of the oncology nurse, as an educator and advocate regarding the implementation of this vaccine is crucial for successful acceptance of this vaccine. Finally, future implications of the vaccine and avenues of research will be touched upon.
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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.005 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.010 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 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".