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Record W4406920685 · doi:10.1177/10598405241312981

Vaccinating Adolescents With Intellectual and Developmental Disability at School: An Opportunity to Promote Supported Decision Making

2025· article· en· W4406920685 on OpenAlexaff
Christiane Klinner, Alexandra M. Young, Iva Strnadová, Jenny O’Neill, Christy E. Newman, Horas Wong, Cristyn Davies, Cassandra Vujovich‐Dunn, S. Rachel Skinner, Deidre Brogan, Melissa Kang, Margie Danchin, Rebecca Guy, Allison Carter

Bibliographic record

VenueThe Journal of School Nursing · 2025
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsFocus groupFeelingVaccinationPsychologyIntellectual disabilityMedical educationPopulationMedicineNursingSocial psychologyPsychiatryEnvironmental health

Abstract

fetched live from OpenAlex

Internationally, vaccination rates among adolescents with intellectual and developmental disability (IDD) are lower than those of the general population. Little research has addressed this issue. This study investigates the experiences of vaccinating adolescents with IDD in special education settings in Australia, with a focus on student engagement. Semistructured interviews and focus groups were undertaken with 50 stakeholders involved in the school vaccination program. Data was analyzed thematically using a framework approach. We identified five themes: lack of student engagement, practices discouraging engagement, practices fostering engagement, lack of vaccination protocol adjustments, and lack of disability-specific nurse training. Nurses' limited knowledge and skills in engaging students with IDD can result in unethical practices, students feeling excluded from vaccination decisions, and students being traumatized by negative treatment experiences. Clinical protocols lack guidance and reasonable adjustments to provide inclusive vaccination services. Supported decision making could provide more inclusive school vaccinations, more positive vaccination experiences, and higher vaccination rates among adolescents with IDD.

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)0.002
metaresearch head score (Gemma)0.004
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 score0.289
Threshold uncertainty score0.868

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.034
GPT teacher head0.350
Teacher spread0.315 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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