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Record W1978375173 · doi:10.1016/j.vaccine.2010.02.035

Canada's National Advisory Committee on Immunization (NACI): Evidence-based decision-making on vaccines and immunization

2010· article· en· W1978375173 on OpenAlexafffundabout
Shainoor J. Ismail, Joanne M. Langley, Tara Harris, Bryna Warshawsky, Shalini Desai, M. FarhangMehr

Bibliographic record

VenueVaccine · 2010
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsMiddlesex London Health UnitDalhousie UniversityIzaak Walton Killam Health CentrePublic Health Agency of Canada
FundersMinistère de la Défense NationalePublic Health Agency of Canada
KeywordsAdvisory committeeImmunizationScientific evidencePublic healthPopulationPolitical scienceMedicinePublic relationsPublic administrationEnvironmental healthImmunologyNursing

Abstract

fetched live from OpenAlex

The National Advisory Committee on Immunization (NACI) provides medical, scientific, and public health advice on the use of vaccines in Canada. This article describes the structure and processes of NACI, as well as its approach to evidence-based decision-making. In a rapidly evolving and complex immunization environment, NACI has faced challenges in its endeavour to make thorough and timely evidence-based recommendations. Making population-level recommendations without formally considering the full spectrum of public health science (e.g. cost-effectiveness) presents difficulties in the implementation of NACI's recommendations. Although an improved and more transparent evidence-based NACI decision-making process is now in place, this is continuing to evolve with a current review of structures and processes underway to further improve effectiveness and efficiencies.

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 score0.519
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.019
GPT teacher head0.295
Teacher spread0.277 · 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

Citations47
Published2010
Admission routes3
Has abstractyes

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