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Ill‐Informed Consent? A Content Analysis of Physical Risk Disclosure in School‐Based <scp>HPV</scp> Vaccine Programs

2011· article· en· W3122221893 on OpenAlexafffundabout
Audrey Steenbeek, Noni E. MacDonald, Jocelyn Downie, Mary Appleton, Françoise Βaylis

Bibliographic record

VenuePublic Health Nursing · 2011
Typearticle
Languageen
FieldMedicine
TopicCervical Cancer and HPV Research
Canadian institutionsNova Scotia Health AuthorityIzaak Walton Killam Health CentreDalhousie University
FundersDalhousie Medical Research Foundation
KeywordsJurisdictionMedicineFamily medicineCLARITYPublic healthNursingPolitical scienceLaw

Abstract

fetched live from OpenAlex

Abstract Objectives This study examines the accuracy, completeness, and consistency of human papilloma virus ( HPV ) vaccine related physical risks disclosed in documents available to parents, legal guardians, and girls in Canadian jurisdictions with school‐based HPV vaccine programs. Design and Sample We conducted an online search for program related HPV vaccine risk/benefit documents for all 13 Canadian jurisdictions between July 2008 and May 2009 including follow‐up by e‐mail and telephone requests for relevant documents from the respective Ministries or Departments of Health. The physical risks listed in the documents were compared across jurisdictions and against documents prepared by the vaccine manufacturer (Merck Frosst Canada), the National Advisory Committee on Immunization ( NACI ), the Society of Obstetricians and Gynecologists of Canada ( SOGC ), and a 2007 article in Maclean's Magazine. Results No jurisdiction provided the same list of vaccine related physical risks as any other jurisdiction. Major discrepancies were identified. Conclusions Inaccurate, incomplete, and inconsistent information can threaten the validity of consent/authorization and potentially undermine trust in the vaccine program and the vaccine itself. Efforts are needed to improve the quality, clarity, and standardization of the content of written documents used in school‐based HPV vaccine programs across Canada.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.415
Threshold uncertainty score0.686

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.215
GPT teacher head0.416
Teacher spread0.200 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations11
Published2011
Admission routes3
Has abstractyes

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