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Record W2051000394 · doi:10.1186/1471-2288-10-51

Pan-Canadian assessment of pandemic immunization data collection: study methodology

2010· article· en· W2051000394 on OpenAlexafffundabout
Jennifer Pereira, Susan Quach, Christine Heidebrecht, Julie Foisy, Sherman Quan, Michael Finkelstein, Christopher Sikora, Julie A. Bettinger, David L. Buckeridge, Anne McCarthy, Shelley L. Deeks, Jeffrey C. Kwong

Bibliographic record

VenueBMC Medical Research Methodology · 2010
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsPublic Health OntarioUniversity Health NetworkOttawa HospitalPublic Health Agency of CanadaInstitute for Clinical Evaluative SciencesMcGill UniversityUniversity of British ColumbiaUniversity of AlbertaToronto Public HealthCentre for Advancing Health OutcomesCentre for Social InnovationUniversity of Toronto
FundersCanadian Institutes of Health ResearchOntario Ministry of Health and Long-Term CarePublic Health AgencyInstitute for Clinical Evaluative SciencesPublic Health Agency of Canada
KeywordsData collectionVaccinationImmunizationUsabilityMedicinePandemicObservational studyFamily medicineMedical emergencyComputer scienceCoronavirus disease 2019 (COVID-19)ImmunologyDiseaseStatistics

Abstract

fetched live from OpenAlex

BACKGROUND: The collection of individual-level pandemic (H1N1) 2009 influenza immunization data was considered important to facilitate optimal vaccine delivery and accurate assessment of vaccine coverage. These data are also critical for research aimed at evaluating the new vaccine's safety and effectiveness. Systems used to collect immunization data include manual approaches in which data are collected and retained on paper, electronic systems in which data are captured on computer at the point of vaccination and hybrid systems which are comprised of both computerized and manual data collection components. This study's objective was to compare the efficiencies and perceptions of data collection methods employed during Canada's pandemic (H1N1) 2009 influenza vaccination campaign. METHODS/DESIGN: A pan-Canadian observational study was conducted in a convenience sample of public health clinics and healthcare institutions during the H1N1 vaccination campaign in the fall of 2009. The study design consisted of three stages: Stage 1 involved passive observation of the site's layout, processes and client flow; Stage 2 entailed timing site staff on 20 clients through five core immunization tasks: i) client registration, ii) medical history collection, iii) medical history review, iv) vaccine administration record keeping and v) preparation of proof of vaccine administration for the client; in Stage 3, site staff completed a questionnaire regarding perceived usability of the site's data collection approach. Before the national study began, a pilot study was conducted in three seasonal influenza vaccination sites in Ontario, to both test that the proposed methodology was logistically feasible and to determine inter-rater reliability in the measurements of the research staff. Comparative analyses will be conducted across the range of data collection methods with respect to time required to collect immunization data, number and type of individual-level data elements collected, and clinic staff perceptions of the usability of the method employed at their site, using analysis of variance (ANOVA). DISCUSSION: Various data collection methods were employed at immunization sites across Canada during the pandemic (H1N1) 2009 influenza vaccination campaign. Our comparison of methods can facilitate planning an efficient, coordinated approach for collecting immunization data in future influenza seasons.

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

Direct model labels (unvalidated)

Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.

Model armCategoriesStudy designConfidence
gemmaMetaresearch
Domain: Methods · Genre: Methods
About the Canadian research system: yes · About a Canadian topic: yes
Not applicablemedium
gptno category
Domain: not available · Genre: Methods
About the Canadian research system: no · About a Canadian topic: yes
Observationalmedium
models splitAgreement compares identical category sets and study designs across arms.

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)0.123
metaresearch head score (Gemma)0.374
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesMetaresearch
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.393
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.1230.374
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.002
Research integrity0.0010.004
Insufficient payload (model declined to judge)0.0040.000

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 head0.856
GPT teacher head0.687
Teacher spread0.169 · 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

Labeled directly by 2 models reading the full record.

Metaresearch

The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.

Study designNot applicable · Observational
DomainMethods
GenreMethods

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

Citations6
Published2010
Admission routes3
Has abstractyes

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