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Record W4404807677 · doi:10.1370/afm.22.s1.6677

Population learning: vaccination against respiratory infections in the McGill sites of the Cohort in Primary Care (COPRI)

2024· article· en· W4404807677 on OpenAlexaboutno aff
Alexandra de Pokomandy, Vladimir Khanassov, Annick Gauthier, Tibor Schuster, Yvan KOSI

Bibliographic record

VenueThe Annals of Family Medicine · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicVaccine Coverage and Hesitancy
Canadian institutionsnot available
Fundersnot available
KeywordsCohortVaccinationPrimary carePopulationCohort studyMedicineRespiratory systemEnvironmental healthImmunologyGeographyDemographyGerontologyFamily medicineInternal medicineSociology

Abstract

fetched live from OpenAlex

Context: Respiratory infections are a prevalent health issue that concerns patients and stresses healthcare resources. Vaccines can prevent several respiratory infections, but the uptake is variable. To improve preventive first line healthcare, we need to understand the uptake and perspective of patients on vaccines. Objectives: 1) Measure the uptake of publicly-funded vaccines against respiratory infections; 2) Understand the hesitancy towards these vaccines in a population of adults followed in Family Medicine Groups (FMGs). Study design and analysis: The Cohort in Primary Care (COPRI) is a prospective study recruiting adults followed in FMGs across Québec. The cohort is meant to be used to support a learning health system, with patient-partners and clinician engagement to improve primary care. In the early phase of the provincial COPRI development, the McGill COPRI team secured funding to investigate vaccines against respiratory infections. Analyses are descriptive with interquartile range (IQR) and 95% confidence intervals (CI). Population studied: COPRI is recruiting adults followed at 7 participating McGill FMGs in Montreal (4), Chateauguay, Val d9Or and Gatineau. Recruitment continues but will close before NAPCRG, final results will be presented. Outcome measures: Participant data was collected from online questionnaires that could be self-administered or completed with research staff. Results: 166 participants were recruited from 6 sites as of April 16 2024, with a median age of 56 (IQR 39-70) years, 31.9% were ?65. 70.5% self-identified as cis-women and 29.5% as cis-men, 70.5% as white, 5.4% as black, 1.8% as Indigenous. The vast majority previously received a vaccine against SARS-CoV-2 (98.8%, 95% CI: 95.2-99.7, median of 4 doses) and influenza (72.3%, 95%CI: 64.9-78.6). Also, 25.9% (95%CI:19.8-33.2) received Prevnar and 23.5% (95%CI 17.6-30.7) received Pneumovax against pneumococcus, and 40.4% (95%CI 33.1-48.1) were vaccinated against Bordetella pertussis. 63.3% (95%CI 55.6-70.3) of the vaccinated said they were likely to get an additional dose of COVID-19 vaccine if recommended. For the 49 (29.5%, 95%CI 23.0-37.0) reporting COVID-19 vaccine hesitancy, 53.1% said that their family doctor or clinic discussing their hesitations could help them gain trust in vaccines. Conclusions: Among McGill COPRI participants, vaccination rates were good despite some hesitancy, which could be improved through discussions with their primary healthcare providers.

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)0.003
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.191
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0020.000
Scholarly communication0.0010.001
Open science0.0020.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.001

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 head0.153
GPT teacher head0.405
Teacher spread0.252 · 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 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".

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Citations0
Published2024
Admission routes1
Has abstractyes

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