{"id":"W4406542142","doi":"10.1136/bmjph-2024-000926","title":"Applying a diffusion of innovations framework to characterise diffusion groups and more effectively reach late adopters: a cross-sectional study on COVID-19 vaccinations in Canada in late 2021","year":2025,"lang":"en","type":"article","venue":"BMJ Public Health","topic":"Vaccine Coverage and Hesitancy","field":"Social Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; University of Calgary","funders":"Canadian Institutes of Health Research; Centre for Research on Pandemic Preparedness and Health Emergencies","keywords":"Multinomial logistic regression; Vaccination; Logistic regression; Early adopter; Pharmacy; Psychology; Coronavirus disease 2019 (COVID-19); Diffusion of innovations; Cross-sectional study; Health care; Demography; Gerontology; Medicine; Social psychology; Family medicine; Marketing; Sociology; Business; Disease; Economics; Economic growth; Statistics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006653714,0.0006222462,0.0005948475,0.00202905,0.004484165,0.002417811,0.001857236,0.0009639734,0.001986785],"category_scores_gemma":[0.01212209,0.0005459322,0.001388925,0.00355378,0.001349392,0.001467868,0.001567429,0.002054472,0.0002137361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02723266,"about_ca_system_score_gemma":0.04302368,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9703706,"about_ca_topic_score_gemma":0.970057,"domain_scores_codex":[0.9973042,0.0005461056,0.0001753728,0.000279941,0.0005734274,0.001120942],"domain_scores_gemma":[0.9937569,0.001079165,0.001374012,0.0002095249,0.002473918,0.001106509],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00007829539,0.0002058953,0.9695929,0.00007821978,0.00003754573,0.00007818823,0.02231062,0.0000762604,0.0001323585,0.0002290833,0.0003704044,0.006810233],"study_design_scores_gemma":[0.00001542009,0.0001613292,0.9641209,0.000124431,0.00004144651,0.00004810011,0.03325375,0.000716485,0.0001074326,0.00009316842,0.001291452,0.00002595678],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9973822,0.0003101302,0.0003255133,0.0003794264,0.00001021564,0.0002525465,0.0004982874,0.000003969777,0.0008375894],"genre_scores_gemma":[0.9974246,0.0002686997,0.0008131128,0.0001824611,0.000004890191,0.0001499698,0.0003573989,0.000005195989,0.0007938071],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02962941,"threshold_uncertainty_score":0.1975877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05061806272259683,"score_gpt":0.402968622279885,"score_spread":0.3523505595572882,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}