{"id":"W2890423698","doi":"10.23889/ijpds.v3i4.660","title":"Using administrative data to examine government service transitions of children, youth and young adults in Alberta, Canada","year":2018,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Healthcare Policy and Management","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Income Support; Government (linguistics); Harm; Service (business); Service delivery framework; Mental health; Business; Psychology; Medicine; Political science; Psychiatry; Social psychology; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001513843,0.0004154199,0.0003561371,0.004235616,0.003004272,0.001891294,0.001984519,0.0004112881,0.001672516],"category_scores_gemma":[0.004152375,0.0004579439,0.0005153797,0.01082214,0.0006863687,0.0004466131,0.001522888,0.0008493229,0.0003331527],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04319199,"about_ca_system_score_gemma":0.05333549,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9984933,"about_ca_topic_score_gemma":0.9989361,"domain_scores_codex":[0.9978103,0.0001719037,0.0001442988,0.0002162358,0.000969185,0.0006881272],"domain_scores_gemma":[0.9956468,0.0002692639,0.0007738357,0.0001552653,0.002303218,0.0008516237],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003780711,0.00003531502,0.9905346,0.00003897744,0.00004271696,0.0000523811,0.001068541,0.0002692721,0.00005990074,0.0001807292,0.002361662,0.005318167],"study_design_scores_gemma":[0.0000043358,0.00001003693,0.994996,0.00003672742,0.00001389715,0.00001786033,0.002640674,0.0004676755,0.00003713906,0.00002311731,0.001745673,0.00000697767],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9680477,0.0008157517,0.0003068485,0.0004993943,0.00002156351,0.000176218,0.02541607,0.00003984014,0.004676427],"genre_scores_gemma":[0.9793944,0.0009500615,0.001219329,0.0002412559,0.00001242256,0.0001424195,0.01569576,0.00001291316,0.002331556],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04319199,"threshold_uncertainty_score":0.3133813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.215893078539556,"score_gpt":0.3777235805440734,"score_spread":0.1618305020045174,"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."}}