{"id":"W4382584858","doi":"10.2196/41204","title":"Key Enablers to Boost Digital Health Solutions in Latin America","year":2023,"lang":"en","type":"article","venue":"Iproceedings","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Latin Americans; Digital health; Health care; Sustainability; Business; Key (lock); Knowledge management; Public relations; Economic growth; Marketing; Political science; Computer science; Economics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.000895892,0.0001987757,0.0003875738,0.0005837593,0.001534998,0.00002702941,0.0002631361,0.0001542103,0.0001903152],"category_scores_gemma":[0.0006761871,0.0002055048,0.00005134155,0.002911404,0.00005317952,0.0002403421,0.0002068025,0.0007032717,0.007519629],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005500003,"about_ca_system_score_gemma":0.001155573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001449607,"about_ca_topic_score_gemma":0.0003607437,"domain_scores_codex":[0.996234,0.00004669809,0.000960435,0.0005322384,0.00031137,0.001915264],"domain_scores_gemma":[0.998121,0.0003379082,0.0002549118,0.0002615329,0.0001715788,0.0008530953],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00006310285,0.0001051824,0.03407618,0.0007886553,0.000006321819,0.000002627799,0.01405953,0.00005567413,0.0001247698,0.019135,0.8490744,0.08250854],"study_design_scores_gemma":[0.0005949948,0.000122662,0.05118198,0.000255634,0.000002110673,8.38736e-7,0.006975177,0.0004646443,0.000001950386,0.001404779,0.9388086,0.0001866612],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.4633059,0.0004702305,0.001472955,0.3576193,0.001926692,0.01847847,0.0006610667,0.003540337,0.152525],"genre_scores_gemma":[0.9295462,0.0007750503,0.001197867,0.0401046,0.0006782982,0.01654553,0.0002513036,0.0001111488,0.01079],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4662403,"threshold_uncertainty_score":0.9997649,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08341381116416319,"score_gpt":0.4177797856779263,"score_spread":0.3343659745137631,"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."}}