{"id":"W2915285122","doi":"10.1177/1178632919827930","title":"Resource Intensity for Children and Youth: The Development of an Algorithm to Identify High Service Users in Children’s Mental Health","year":2019,"lang":"en","type":"article","venue":"Health Services Insights","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Western University","funders":"Cancer Prevention and Research Institute of Texas","keywords":"Mental health; Service (business); Resource (disambiguation); Sample (material); Resource allocation; Computer science; Psychology; Nursing; Medical education; Medicine; Psychiatry; Business","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":[0.01018011,0.0008907766,0.0009101242,0.005351509,0.000981832,0.001833705,0.001340283,0.0006096084,0.001555529],"category_scores_gemma":[0.02133229,0.0004720092,0.001601308,0.002528545,0.0006604437,0.00146871,0.002396098,0.0009674061,0.0004232768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003626007,"about_ca_system_score_gemma":0.006695375,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05762315,"about_ca_topic_score_gemma":0.07201981,"domain_scores_codex":[0.9959776,0.001587568,0.00085054,0.0003383736,0.0009262235,0.0003197555],"domain_scores_gemma":[0.9901121,0.003560544,0.002214246,0.0004114751,0.003355675,0.0003459199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000265978,0.0002128191,0.9215505,0.0001428893,0.0001393844,0.00007316536,0.001368652,0.002532785,0.0005161426,0.0009431533,0.003061949,0.06919263],"study_design_scores_gemma":[0.0002115306,0.0006171485,0.9077192,0.0003009031,0.0002137063,0.0003538425,0.004802221,0.07178141,0.002997618,0.002417707,0.008461211,0.0001234885],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.8847951,0.00067847,0.09069343,0.001176468,0.00005121543,0.007076439,0.005159357,0.0006652538,0.009704158],"genre_scores_gemma":[0.6692764,0.000326646,0.3204697,0.0001394981,0.00001835948,0.004357723,0.004061163,0.00006843008,0.001282046],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.05762315,"threshold_uncertainty_score":0.1145755,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01531373780217161,"score_gpt":0.3109684134019041,"score_spread":0.2956546755997325,"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."}}