{"id":"W7133023975","doi":"","title":"Modeling Supply and Demand of General Internists in Ontario","year":2024,"lang":"","type":"dissertation","venue":"TSpace","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Workforce; Specialty; Supply and demand; Population; Workforce planning; Service (business); Census; Physician supply; Health care","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":[0.0004429216,0.0005197756,0.0003580714,0.0004207907,0.0009664536,0.001203634,0.0009917018,0.0009464903,0.004527379],"category_scores_gemma":[0.001408955,0.0004578688,0.0006404897,0.000905994,0.0005498001,0.0005360219,0.0006825633,0.0005814933,0.0002874005],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01820393,"about_ca_system_score_gemma":0.01057644,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9028314,"about_ca_topic_score_gemma":0.8711681,"domain_scores_codex":[0.9997417,0.00004649115,0.000009051412,0.00004469424,0.00004620133,0.0001117838],"domain_scores_gemma":[0.999523,0.0002052238,0.00006368276,0.00001493209,0.0001241614,0.00006903363],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00007627907,0.00005428322,0.01590094,0.00003953164,0.00002231224,0.0001705891,0.0002953077,0.9729632,0.0005125515,0.004974942,0.001874763,0.003115378],"study_design_scores_gemma":[0.00002655729,0.00003932496,0.01194037,0.00001098462,0.00001282837,0.00001351497,0.0005905587,0.983938,0.0001061714,0.001059127,0.002248756,0.00001380757],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9556521,0.0001980873,0.00832838,0.001380586,0.00002567637,0.0001120431,0.003941956,0.0000618373,0.03029933],"genre_scores_gemma":[0.9877899,0.0002017079,0.0019862,0.00005030953,0.000009612699,0.00005550428,0.001124334,0.00001388311,0.008768495],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9817961,"threshold_uncertainty_score":0.1954817,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07889036207997047,"score_gpt":0.4529656543525035,"score_spread":0.374075292272533,"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."}}