{"id":"W4210496520","doi":"10.2196/preprints.14849","title":"The Service of Research Analytics to Optimize Digital Health Evidence Generation: Multilevel Case Study (Preprint)","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; Women's College Hospital; Public Health Ontario; University of Toronto; University Health Network","funders":"","keywords":"Analytics; Digital health; Psychological intervention; Data science; Knowledge management; Computer science; Service (business); Health care; Medicine; Nursing; Political science; 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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05965037,0.0003178247,0.0004950053,0.001882917,0.01034927,0.007925706,0.002561767,0.002842287,0.007519871],"category_scores_gemma":[0.09740914,0.0005845036,0.0009765676,0.00318257,0.007293831,0.006045504,0.01006509,0.002795896,0.0009184022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01783233,"about_ca_system_score_gemma":0.02981599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01973765,"about_ca_topic_score_gemma":0.04614777,"domain_scores_codex":[0.9410076,0.05111044,0.001702951,0.00133643,0.002865188,0.001977469],"domain_scores_gemma":[0.8576229,0.1119685,0.007638986,0.008086375,0.00772292,0.006960239],"domain_codex":null,"domain_gemma":"evaluation","domain_candidate":"evaluation","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0005888749,0.002674384,0.07064846,0.003265497,0.0001937254,0.009711874,0.60954,0.002680324,0.002502069,0.1086817,0.03284793,0.1566652],"study_design_scores_gemma":[0.000481832,0.0018826,0.03626638,0.004437295,0.0002517903,0.002403816,0.6965245,0.00838675,0.004184421,0.03373767,0.2111812,0.000261745],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7817432,0.002202931,0.05801236,0.08761055,0.0004868823,0.01114413,0.00115571,0.0002769287,0.05736732],"genre_scores_gemma":[0.9073095,0.00119446,0.07683223,0.003773676,0.0001421116,0.005474377,0.0001939355,0.0000652461,0.00501452],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9403496,"threshold_uncertainty_score":0.315465,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4755911821507637,"score_gpt":0.5931876145284951,"score_spread":0.1175964323777314,"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."}}