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Record W3150165977 · doi:10.3390/vaccines9040312

Achieving Influenza Vaccine Uptake Target in Canada via a Pharmacy-Led Telephone Discussion during the 2019–2020 Season

2021· article· en· W3150165977 on OpenAlexaffabout
W. David Strain, James A. Mansi, Constantina Boikos, Michael Boivin, William A. Fisher

Bibliographic record

VenueVaccines · 2021
Typearticle
Languageen
FieldMedicine
TopicInfluenza Virus Research Studies
Canadian institutionsBarrie Urology GroupWestern UniversityCentre for Advancing Health Outcomes
FundersNIHR Exeter Clinical Research FacilityDepartment of Health and Social CareNational Institute for Health and Care Research
KeywordsVaccinationMedicinePharmacyFamily medicinePharmacistTelephone surveyImmunizationPhoneInfluenza vaccineHealth careFlu seasonCommunity pharmacistInfluenza seasonImmunology

Abstract

fetched live from OpenAlex

Older adults (≥65 years) are at elevated risk of influenza-related morbidity and mortality. Many developed countries do not achieve the World Health Organization influenza immunization target of 75% in people ≥65 years. We aimed to determine whether a brief pharmacy phone call could increase vaccine uptake of standard and enhanced influenza. Twenty-eight community pharmacists across Canada performed a telephone consultation with 643 older adults whose primary care records indicated that they had not received their influenza vaccination from their usual practitioner. Of these 643 adults, 169 (26.3%) had been vaccinated in another setting. Of the remaining 474, 313 (66%) agreed to receive the vaccine. Of those who refused vaccination, 69 provided a rationale for not wanting it, including that the flu shot “causes the flu” (n = 25), “doesn’t work” (n = 25), “is too painful” (n = 10), and other (n = 10). Overall, of the 643 individuals who had not received their vaccination from their usual health care provider in the first wave of vaccinations, 75.4% (n = 485) ultimately received their vaccination in the 2019–2020 season. This highlights the important role of the community pharmacist in achieving the World Health Organization (WHO) targets for vaccination.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.325
Threshold uncertainty score0.904

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.022
GPT teacher head0.316
Teacher spread0.294 · 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

Citations11
Published2021
Admission routes2
Has abstractyes

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