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Record W2066050285 · doi:10.3390/jcm2040242

How do Midwives and Physicians Discuss Childhood Vaccination with Parents?

2013· article· en· W2066050285 on OpenAlexafffundabout
Ève Dubé, Maryline Vivion, Chantal Sauvageau, Arnaud Gagneur, Raymonde F. Gagnon, Maryse Guay

Bibliographic record

VenueJournal of Clinical Medicine · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsUniversité du Québec à Trois-RivièresUniversité de SherbrookeUniversité LavalInstitut National de Santé Publique du Québec
FundersCanadian Institutes of Health Research
KeywordsMedicineVaccinationFamily medicinePublic healthPregnancyNursingImmunology

Abstract

fetched live from OpenAlex

Even if vaccination is often described as one of the great achievements of public health, results of recent studies have shown that parental acceptance of vaccination is eroding. Health providers' knowledge and attitudes about vaccines are important determinants of their own vaccine uptake, their intention to recommend vaccines to patients and the vaccine uptake of their patients. The purpose of this article is to compare how midwives and physicians address vaccination with parents during pregnancy and in postpartum visits. Thirty semi-structured interviews were conducted with midwives and physicians practicing in the province of Quebec, Canada. Results of our analysis have shown that physicians adopt an "education-information" stance when discussing vaccination with parents in the attempt to "convince" parents to vaccinate. In contrast, midwives adopted a neutral stance and gave information on the pros and cons of vaccination to parents while leaving the final decision up to them. Findings of this study highlight the fact that physicians and midwives have different views regarding their role and responsibilities concerning vaccination. It may be that neither of these approaches is optimal in promoting vaccination uptake.

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)0.002
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 score0.453
Threshold uncertainty score0.223

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.029
GPT teacher head0.366
Teacher spread0.337 · 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

Citations40
Published2013
Admission routes3
Has abstractyes

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