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Record W2110159970

H1N1 and influenza viruses: why pregnant women might be hesitant to be vaccinated.

2011· article· en· W2110159970 on OpenAlexaboutno aff
Kamelia Mirdamadi, Adrienne Einarson

Bibliographic record

VenuePubMed · 2011
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineVaccinationSensationalismPregnancyFamily medicinePopulationInfluenza vaccineH1N1 influenzaPandemicImmunologyEnvironmental healthCoronavirus disease 2019 (COVID-19)Political scienceDisease
DOInot available

Abstract

fetched live from OpenAlex

QUESTION: I have been encouraging pregnant women to receive both the H1N1 and influenza vaccines since I became aware of Health Canada's guidelines. However, some of the women in my practice have heard conflicting information, often from media sources, and they are hesitant to be vaccinated. What is the evidence behind these guidelines, and should I really be convincing these women to be vaccinated? ANSWER: Pregnant women and growing fetuses are considered a population vulnerable to H1N1 and influenza viruses. Health Canada published a report in late 2010 estimating that this population was at increased risk of hospitalization and severe outcomes of H1N1 infection. Recommendations included pregnant women as a priority group to receive the H1N1 vaccine as well as the influenza vaccine. This information should be explained unambiguously to pregnant women, and they should be made aware of the sensationalism of media reports, which are often based on opinion and not evidence.

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 imitation

Not 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.

metaresearch head score (Codex)0.011
metaresearch head score (Gemma)0.145
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.012
Threshold uncertainty score0.058

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.145
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.004
Open science0.0010.001
Research integrity0.0070.003
Insufficient payload (model declined to judge)0.0080.002

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 head0.180
GPT teacher head0.331
Teacher spread0.151 · 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 source (direct Gemma or distilled Codex), 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

Citations7
Published2011
Admission routes1
Has abstractyes

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