{"id":"W4290839821","doi":"10.2196/35612","title":"National Development and Regional Differences in eHealth Maturity in Finnish Public Health Care: Survey Study","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":36,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Terveyden ja hyvinvoinnin laitos","keywords":"eHealth; Health care; Maturity (psychological); Information and Communications Technology; Health informatics; Public health; Business; Health information exchange; Medicine; Nursing; Environmental health; Health information; Psychology; Computer science; World Wide Web; Economic growth","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003510346,0.0003087016,0.0004373742,0.002563931,0.0008156769,0.001092667,0.0007762678,0.000550803,0.001992235],"category_scores_gemma":[0.005700526,0.0004443329,0.001184703,0.004769973,0.0005766425,0.001038268,0.00154547,0.000483227,0.0003138737],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002344745,"about_ca_system_score_gemma":0.002597313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06146515,"about_ca_topic_score_gemma":0.05486245,"domain_scores_codex":[0.997614,0.0005711475,0.0004458374,0.0004196056,0.000385602,0.0005638688],"domain_scores_gemma":[0.9958913,0.001146994,0.001483225,0.0002766478,0.0006775322,0.0005242405],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00004175576,0.00005048965,0.9960287,0.00005853473,0.0000619902,0.00007228343,0.001855746,0.00004661507,0.00006373147,0.00002843447,0.0001392167,0.001552619],"study_design_scores_gemma":[0.000003384202,0.00007158888,0.9946899,0.00002403121,0.00003547992,0.00007452276,0.004744707,0.00007692519,0.00003811474,0.000009500066,0.0002255289,0.000006355262],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9981135,0.0001632533,0.00007924899,0.00003554358,0.000003217923,0.00004702655,0.001142089,0.000002337898,0.0004137382],"genre_scores_gemma":[0.9980711,0.0001854611,0.0001922114,0.00003720868,0.000005401301,0.0001078169,0.001272756,0.000001908393,0.0001261343],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06146515,"threshold_uncertainty_score":0.1222147,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1747120290703404,"score_gpt":0.46059672332195,"score_spread":0.2858846942516096,"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."}}