{"id":"W4400923706","doi":"10.23889/ijpds.v9i1.2370","title":"Public sector health analytics capacity before and after Covid-19: A case study of manager perspectives in New Brunswick, Canada","year":2024,"lang":"en","type":"article","venue":"International Journal for Population Data Science","topic":"Public Health Policies and Education","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; University of New Brunswick; Government of New Brunswick","funders":"","keywords":"Analytics; Business; Workforce; Surge Capacity; Promotion (chess); Public sector; Workforce planning; Public relations; Workforce development; Capacity building; Health care; Marketing; Medicine; Data science; Coronavirus disease 2019 (COVID-19); Politics; Political science; Computer science","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":[0.003938504,0.0005754124,0.0005498755,0.001910318,0.03641142,0.007417903,0.003033627,0.001865215,0.00498523],"category_scores_gemma":[0.006787688,0.000679017,0.0005064554,0.003921246,0.006933627,0.001783774,0.005315925,0.003828911,0.0003471826],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.2358664,"about_ca_system_score_gemma":0.3248484,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9940002,"about_ca_topic_score_gemma":0.9979917,"domain_scores_codex":[0.9921171,0.001464527,0.0001940458,0.0004154498,0.001177281,0.004631616],"domain_scores_gemma":[0.9900081,0.001464677,0.0005615229,0.0001530187,0.002648364,0.005164281],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002306415,0.0003328137,0.1351277,0.0004289815,0.00006531764,0.01450673,0.7739182,0.0005837582,0.002056733,0.008226627,0.03125202,0.03327059],"study_design_scores_gemma":[0.00001229371,0.00007382537,0.03893503,0.0002684352,0.00002224093,0.0005044539,0.9163034,0.0003317044,0.0002595775,0.0002780254,0.04295367,0.00005731378],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9375451,0.002524244,0.0007861655,0.0330591,0.0002206514,0.0004263121,0.0006319933,0.0000522681,0.02475419],"genre_scores_gemma":[0.9830974,0.001550269,0.0008431803,0.004823562,0.0000378368,0.00009465667,0.0001634297,0.00003840832,0.009351291],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7641336,"threshold_uncertainty_score":0.8862867,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2833849813118122,"score_gpt":0.5304744255168906,"score_spread":0.2470894442050784,"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."}}