{"id":"W3098799676","doi":"10.1101/2020.11.11.20229393","title":"Using Convergent Sequential Design for Rapid Complex Case Study Descriptions: Example of Public Health Briefings During the Onset of the COVID-19 Pandemic","year":2020,"lang":"en","type":"preprint","venue":"medRxiv","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"University of Alberta; Government of Alberta","keywords":"Interdependence; Pandemic; Public health; Computer science; Adaptation (eye); Coronavirus disease 2019 (COVID-19); Complex adaptive system; Data science; Key (lock); Management science; Theoretical computer science; Artificial intelligence; Sociology; Psychology; Medicine; Computer security; Social science; Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.08238106,0.0008834709,0.0007771172,0.003294158,0.004802238,0.004775551,0.003008745,0.002592633,0.01477264],"category_scores_gemma":[0.1377825,0.0006116116,0.001602158,0.003931304,0.004467178,0.003629806,0.005745235,0.002867536,0.001309424],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005733051,"about_ca_system_score_gemma":0.006795591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003595225,"about_ca_topic_score_gemma":0.008081742,"domain_scores_codex":[0.9082897,0.08274198,0.002682068,0.001908685,0.003629884,0.0007475955],"domain_scores_gemma":[0.6298971,0.3373024,0.00517947,0.01368097,0.01235212,0.001588078],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"qualitative","study_design_scores_codex":[0.001680925,0.001615235,0.01080089,0.005725625,0.0002851847,0.004217884,0.1887624,0.02916006,0.005481383,0.4875578,0.02752055,0.237192],"study_design_scores_gemma":[0.001315301,0.00190181,0.007983015,0.004968894,0.0002669247,0.001124696,0.131836,0.07855874,0.01249094,0.4629053,0.2962763,0.0003720093],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1262158,0.0008244379,0.7952932,0.009940016,0.0005392283,0.0182412,0.004188062,0.0006940458,0.04406398],"genre_scores_gemma":[0.202808,0.0003530991,0.7721267,0.0007551748,0.00007311658,0.01928855,0.001180906,0.0001479042,0.003266538],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.08238106,"threshold_uncertainty_score":0.4356779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9366064553698881,"score_gpt":0.6496563817647784,"score_spread":0.2869500736051097,"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."}}