{"id":"W4282946985","doi":"10.2196/29930","title":"Digital Health Solutions and State of Interoperability: Landscape Analysis of Sierra Leone","year":2022,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Università ta' Malta; United States Agency for International Development; United Nations Population Fund; UNICEF; Johns Hopkins University; World Health Organization","keywords":"Sierra leone; Digital health; Interoperability; HRHIS; Health care; Health informatics; Health facility; Telemedicine; Community health; Public health informatics; Health policy; Government (linguistics); Business; Environmental health; Medicine; Computer science; Public health; Nursing; World Wide Web; Political science; Health services; Socioeconomics; Population; Sociology","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":[0.001759413,0.0001986479,0.000215031,0.004616272,0.001341006,0.004570266,0.0006390053,0.0004671672,0.002155411],"category_scores_gemma":[0.005472279,0.0001445394,0.0001712719,0.004603612,0.002153647,0.002457469,0.002696271,0.0003562586,0.00007483045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003731451,"about_ca_system_score_gemma":0.002007716,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04931306,"about_ca_topic_score_gemma":0.05082608,"domain_scores_codex":[0.9985809,0.0006121887,0.00005636737,0.0001058399,0.0002066855,0.0004379536],"domain_scores_gemma":[0.9979568,0.0009178433,0.0004574609,0.0001094301,0.0003507893,0.0002077232],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003833545,0.0002759272,0.6320747,0.001064788,0.0001391065,0.002735168,0.08040202,0.002270689,0.001869384,0.05228132,0.004558898,0.2219446],"study_design_scores_gemma":[0.00002874043,0.0002130568,0.7715908,0.0008863562,0.00007767765,0.0008625691,0.1776208,0.001865779,0.0003479228,0.004013024,0.04245083,0.00004254055],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9773392,0.001866975,0.0004165025,0.001808032,0.000006489186,0.00007370858,0.0002380041,0.00001372712,0.01823732],"genre_scores_gemma":[0.9987821,0.0004444268,0.000247568,0.00005592089,0.000002531604,0.00001862105,0.00009480964,0.000003072099,0.0003508884],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04931306,"threshold_uncertainty_score":0.09805208,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1185764841339924,"score_gpt":0.5099701702110007,"score_spread":0.3913936860770082,"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."}}