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Record W1940713223 · doi:10.1017/s0950268815001909

Reasons for measles cases not being vaccinated with MMR: investigation into parents' and carers' views following a large measles outbreak

2015· article· en· W1940713223 on OpenAlexaboutno aff
Philip McHale, A. Keenan, Sam Ghebrehewet

Bibliographic record

VenueEpidemiology and Infection · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsMeaslesMMR vaccineMedicineRubellaVaccinationMeasles-Mumps-Rubella VaccineOutbreakQuarter (Canadian coin)AutismPediatricsFamily medicineRubella vaccineMeasles vaccineImmunologyPsychiatryVirology

Abstract

fetched live from OpenAlex

Uptake rates for the combined measles, mumps and rubella (MMR) vaccine have been below the required 95% in the UK since a retracted and discredited article linking the MMR vaccine with autism and inflammatory bowel disease was released in 1998. This study undertook semi-structured telephone interviews among parents or carers of 47 unvaccinated measles cases who were aged between 13 months and 9 years, during a large measles outbreak in Merseyside. Results showed that concerns over the specific links with autism remain an important cause of refusal to vaccinate, with over half of respondents stating this as a reason. A quarter stated child illness during scheduled vaccination time, while other reasons included general safety concerns and access issues. Over half of respondents felt that more information or a discussion with a health professional would help the decision-making process, while a third stated improved access. There was clear support for vaccination among respondents when asked about current opinions regarding MMR vaccine. The findings support the hypothesis that safety concerns remain a major barrier to MMR vaccination, and also support previous evidence that experience of measles is an important determinant in the decision to vaccinate.

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)0.003
metaresearch head score (Gemma)0.005
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 score0.106
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
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 head0.089
GPT teacher head0.361
Teacher spread0.272 · 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

Citations38
Published2015
Admission routes1
Has abstractyes

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