MétaCan
Menu
Back to cohort
Record W2902750955 · doi:10.1093/ofid/ofy210.2123

2470. The Effect of Information–Motivation–Behavioral Skills Model-Based Continuing Medical Education on Pediatric Influenza Immunization Uptake: A Randomized, Controlled Trial

2018· article· en· W2902750955 on OpenAlexaffabout
William A. Fisher, John Yaremko, Vivien Brown, Hartley Garfield, Emmanuouil Rampakakis, Constantina Boikos, James A. Mansi

Bibliographic record

VenueOpen Forum Infectious Diseases · 2018
Typearticle
Languageen
FieldHealth Professions
TopicSchool Health and Nursing Education
Canadian institutionsMerck Canada Inc. (Canada)Hospital for Sick ChildrenSickKids FoundationUniversity of TorontoMcGill UniversityWestern University
Fundersnot available
KeywordsMedicineVaccinationImmunizationInfluenza vaccineFamily medicineRandomized controlled trialLogistic regressionPopulationLive attenuated influenza vaccineContinuing medical educationPediatricsPublic healthImmunologyNursingInternal medicineEnvironmental healthContinuing education

Abstract

fetched live from OpenAlex

Seasonal vaccination against influenza is the most important public health strategy to prevent influenza morbidity and mortality in children 6–23 months of age. However, influenza immunization uptake in this population remains sub-optimal. While parents look to healthcare professionals (HCPs) for guidance, HCPs may be neither aware of the burden of influenza disease in infants nor familiar with ways to address parental influenza vaccine hesitancy. The objective of this research was to describe the impact of an Information—Motivation—Behavioral Skills model (IMB)-based, accredited, online Continuing Medical Education (CME) program on seasonal influenza vaccination in children 6–23 months of age in Ontario, Canada during the 2016/2017 influenza season. A multi-center, randomized, controlled trial was conducted whereby HCPs were randomized to either an accredited IMB-based CME or to routine practice (no CME). The CME addressed influenza burden in young children and identified parental barriers (hesitancy) to influenza vaccination, designed to inform, motivate, and upskill HCPs. All vaccine options were reviewed, including the adjuvanted, trivalent, inactive, influenza vaccine (aTIV). Immunization rates were compared between groups using Pearson’s chi-squared and a logistic regression model adjusting for socioeconomic status at the clinic-level. A total of 68 HCPs were recruited: 33 randomized to the CME group and 35 to routine practice. HCP interactions with parents were evaluated during 628 visits: 292 visits by HCPs in the CME group and 336 by HCPs in the routine practice group. Parents seen by HCPs in the CME group were ~30% more likely to agree to immunize their child with seasonal influenza vaccination compared with parents seen by HCPs in the control group (P = 0.007). The adjusted odds of influenza immunization were 1.5 times higher in the CME group compared with the control group. Children in the CME group were ~20% more likely to receive aTIV compared with children in the control group (P < 0.001). HCP education with a tailored health behavior uptake model based CME addressing the burden of influenza disease in young children and influenza vaccine hesitancy was associated with a significant increase in influenza immunization. W. Fisher, Seqirus: Consultant and Investigator, Consulting fee and Speaker honorarium. J. Yaremko, Seqirus: Collaborator and Investigator, Speaker honorarium. V. Brown, Seqirus: Investigator, Speaker honorarium. H. Garfield, Seqirus: Investigator, Speaker honorarium. E. Rampakakis, Seqirus: Independent Contractor, Consulting fee. C. Boikos, Seqirus: Employee, Salary. J. A. Mansi, Seqirus: Employee and Shareholder, Salary.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Randomized trial · Consensus signal: Randomized trial
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.034

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.003
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.016
GPT teacher head0.409
Teacher spread0.394 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designRandomized trial
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

Citations1
Published2018
Admission routes2
Has abstractyes

Explore more

Same venueOpen Forum Infectious DiseasesSame topicSchool Health and Nursing EducationFrench-language works237,207