Human Papillomavirus vaccine uptake: Misinformation online and the healthcare providers’ role in reducing antivaccine sentiment
Bibliographic record
Abstract
Human Papillomavirus (HPV) infections are one of the most common sexually transmitted infections in Canada (Government of Canada, 2020). HPV infections are often easy to treat, however, certain strains of the virus can progress and make the population more susceptible to different cancer diagnosis (2020). This is a concern, as cancer diagnoses related to HPV infection are expected to rise in Canada (Canadian Partnership Against Cancer, 2021). In 2008, Alberta implemented an HPV school vaccine program with young girls as the target group (Highet, Jessiman-Peerault, Hilton, Law & Allen-Scott,2020). Following this, the school vaccine program was expanded to include boys in the same age group (2020). In 2020, the province made a vaccine program inclusive of individuals between ages 18-26 to help promote uptake in this group (2020). Despite school vaccine programs and well-established efficacy and safety research of the vaccine, HPV vaccination levels remain suboptimal in Alberta and different parts of Canada (2020). This is attributable to several causes, however the spread of vaccine misinformation online in addition to the growing influence social media sites have on the public to obtain health information are identified as concerns (Ortiz, Smith & Coyne-Beasley, 2019). This paper will delve into the growing HPV antivaccine sentiment in Canada and its impact on health outcomes. In addition, it will discuss the important role healthcare workers have in addressing vaccine misinformation in efforts to improve vaccination rates. Healthcare providers remain the most trusted individuals to help reduce vaccine misinformation (Paterson et al., 2016). Their innate trust and title legitimize their claims and motivates individuals to value their advice and recommendations. In addition, this paper will discuss the current legislation set in place to regulate healthcare workers in Alberta. It will also offer an analysis of the already established social media documents that guide healthcare workers. The paper ends with a list of policy recommendations to mobilize healthcare workers and ensure that their voices are heard and utilized to help slow and address the spread of vaccine misinformation on social media in hopes of reducing antivaccine sentiment.
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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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".