{"id":"W4403471759","doi":"10.21203/rs.3.rs-5270581/v1","title":"Building Responsive Intersectoral Initiatives for Newcomers in Toronto: Learning from Service Providers’ Experiences in the Context of COVID-19","year":2024,"lang":"en","type":"preprint","venue":"Research Square","topic":"Migration, Health and Trauma","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal; University of Toronto","funders":"","keywords":"Coronavirus disease 2019 (COVID-19); Context (archaeology); Service provider; Business; Service (business); Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2); Public relations; 2019-20 coronavirus outbreak; Knowledge management; Political science; Marketing; Geography; Computer science; Medicine; Virology; Outbreak","routes":{"ca_aff":true,"ca_fund":false,"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":[0.01163697,0.0008163728,0.0005239135,0.001048373,0.03307689,0.01697688,0.00342717,0.00361083,0.01363746],"category_scores_gemma":[0.01392907,0.0007468481,0.0004669738,0.001951732,0.01957069,0.009296635,0.0227242,0.008060859,0.001144039],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0502883,"about_ca_system_score_gemma":0.08848679,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.346292,"about_ca_topic_score_gemma":0.7695174,"domain_scores_codex":[0.9889599,0.006858554,0.0001383435,0.0005149984,0.0007944518,0.0027338],"domain_scores_gemma":[0.9802037,0.005526111,0.001049183,0.0008950417,0.001443586,0.01088236],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004403153,0.00009308186,0.003744462,0.00009773426,0.000008687241,0.0005167184,0.9709697,0.00008953115,0.0001825284,0.006192413,0.00685689,0.01120434],"study_design_scores_gemma":[0.000005831092,0.00002878322,0.001868483,0.00009907702,0.000005954193,0.00003091867,0.9754788,0.00004194069,0.00009409928,0.0007536229,0.02158217,0.0000103677],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.876168,0.00163086,0.00293788,0.044124,0.0004623969,0.000337142,0.0001799763,0.000122445,0.07403737],"genre_scores_gemma":[0.9806189,0.001072639,0.002050173,0.001111725,0.00003153434,0.0001397222,0.00009243891,0.00007419413,0.01480868],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.653708,"threshold_uncertainty_score":0.6885528,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1813292469005844,"score_gpt":0.5202339976999305,"score_spread":0.3389047507993461,"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."}}