{"id":"W2340602013","doi":"10.3390/ijerph13040419","title":"Multi-Stakeholder Decision Aid for Improved Prioritization of the Public Health Impact of Climate Sensitive Infectious Diseases","year":2016,"lang":"en","type":"article","venue":"International Journal of Environmental Research and Public Health","topic":"Zoonotic diseases and public health","field":"Medicine","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"Group for Research in Decision Analysis; Institut National de Santé Publique du Québec; Ouranos; Cegep de Saint Hyacinthe; Public Health Agency of Canada; Université Laval; Université de Montréal","funders":"Canadian Institutes of Health Research","keywords":"Prioritization; Stakeholder; Multiple-criteria decision analysis; Context (archaeology); Ranking (information retrieval); Business; Stakeholder engagement; Environmental planning; Environmental resource management; Infectious disease (medical specialty); Disease; Process management; Political science; Computer science; Medicine; Geography; Operations research; Public relations; Economics; Engineering","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":[0.02586538,0.00133985,0.001298348,0.003620038,0.002645172,0.004788438,0.001965714,0.001492727,0.01904091],"category_scores_gemma":[0.02709884,0.000676702,0.001101274,0.002722087,0.0007411093,0.002970929,0.005440406,0.00179452,0.001256228],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003109654,"about_ca_system_score_gemma":0.00661496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005233623,"about_ca_topic_score_gemma":0.01683182,"domain_scores_codex":[0.9826781,0.01430291,0.0006058299,0.0005515534,0.001397386,0.0004641811],"domain_scores_gemma":[0.9730071,0.02091259,0.001149874,0.0009010784,0.002925779,0.001103572],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.003803756,0.002519472,0.01989825,0.003462459,0.0006865801,0.002101151,0.02145938,0.2163828,0.02381255,0.0678617,0.03150663,0.6065053],"study_design_scores_gemma":[0.0006088365,0.000804649,0.007815853,0.001557668,0.0003444153,0.0003848618,0.02098154,0.7933139,0.01285311,0.08018889,0.08083298,0.00031333],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2479928,0.0008828494,0.6819434,0.007569968,0.0003008938,0.00479761,0.003746103,0.00262453,0.05014195],"genre_scores_gemma":[0.3519463,0.0001972589,0.64351,0.0002640831,0.00002121811,0.001017179,0.0005837782,0.00005938686,0.002400709],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02586538,"threshold_uncertainty_score":0.1367908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08792664309993124,"score_gpt":0.4175259839450351,"score_spread":0.3295993408451038,"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."}}