{"id":"W2265068495","doi":"10.1186/s12889-016-2749-y","title":"A multilevel analysis to explain self-reported adverse health effects and adaptation to urban heat: a cross-sectional survey in the deprived areas of 9 Canadian cities","year":2016,"lang":"en","type":"article","venue":"BMC Public Health","topic":"Climate Change and Health Impacts","field":"Environmental Science","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ouranos; Institut National de Santé Publique du Québec; Université Laval; Centre hospitalier universitaire de Québec; Institut National de la Recherche Scientifique","funders":"Institut National de Santé Publique du Québec","keywords":"Neighbourhood (mathematics); Residence; Multilevel model; Environmental health; Index (typography); Disadvantaged; Public health; Thermal comfort; Biostatistics; Adaptation (eye); Medicine; Cross-sectional study; Geography; Demography; Gerontology; Psychology; Statistics; Economic growth; Meteorology","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":[0.001763281,0.0004173299,0.0004611867,0.001640523,0.002221577,0.00121664,0.0009772885,0.0003873792,0.002056234],"category_scores_gemma":[0.004632464,0.00035464,0.001648306,0.002480417,0.0004678877,0.0004556943,0.001311541,0.0008067821,0.0001453261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01118644,"about_ca_system_score_gemma":0.01371549,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9676023,"about_ca_topic_score_gemma":0.9709219,"domain_scores_codex":[0.9989094,0.0002338333,0.00005270572,0.0001459449,0.0003514632,0.0003065943],"domain_scores_gemma":[0.9980891,0.0003568194,0.0003136934,0.000162883,0.0008038673,0.0002736696],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003094279,0.00003440631,0.9953814,0.00002179837,0.0001716258,0.0000354352,0.0007761031,0.0004712667,0.00008279384,0.0001252749,0.000491776,0.002377192],"study_design_scores_gemma":[0.000005275748,0.00003035277,0.9926054,0.00004150489,0.00008753113,0.00002310454,0.001914475,0.004621055,0.00003716275,0.00008236681,0.0005394823,0.0000123292],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956129,0.0002129821,0.0009112796,0.0002648341,0.000007373783,0.0001219757,0.001859501,0.00001772398,0.0009914659],"genre_scores_gemma":[0.9975382,0.0001022116,0.001210928,0.00003143323,0.000002588629,0.00006861886,0.000759444,0.00000482564,0.0002817728],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03239769,"threshold_uncertainty_score":0.08116364,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.160631431476346,"score_gpt":0.3466518580633121,"score_spread":0.1860204265869661,"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."}}