Beyond the responsibility binary: analysing maternal responsibility in the human papillomavirus vaccination decision
Bibliographic record
Abstract
With the human papillomavirus (HPV) vaccine positioned as the "right tool" to protect girls' health and sexual health, public discourse positions parents as "responsible" if they vaccinate, "irresponsible" if they do not. The problem with this binary, however, is that it cannot account for the full spectrum of responsibilities and social norms that parents enact in vaccine decisions. In this paper, and in the context of low HPV vaccination rates, I confront this binary and encourage a fuller view of adolescent health and sexual health. Using data from qualitative semi-structured interviews with 28 Canadian mothers tasked with consenting to the HPV vaccine, I examine the complexity of this responsibility. I find HPV vaccine-consenting mothers have normative conceptualisations of responsibility aligned with dominant interpretations of public health. Rather than expressing irresponsibility, some non-HPV vaccine-consenting mothers articulated alternate responsibilities, aligned with broad efforts to manage their teens' sexual health and sexuality. They extend responsibility beyond cancer protection vis-à-vis vaccines to a general responsibility for daughters' sexual health and self-esteem. In conclusion, I recommend the need for a broader public health approach to HPV, which includes, and goes beyond vaccination. Moreover, I suggest that some of these alternate responsibilities be viewed as complementary to vaccination.
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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 | 0.013 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.015 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".