{"id":"W4388827161","doi":"10.5539/cis.v16n4p21","title":"Electronic Health System Integration Framework for Secure M-Health Services: A Case of University of Nairobi Hospital","year":2023,"lang":"en","type":"article","venue":"Computer and Information Science","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Mount Kenya University","keywords":"Confidentiality; Computer science; Design science research; Information system; Healthcare system; Data collection; University hospital; Population; Health care; Knowledge management; Computer security; Medicine; Medical emergency; Sociology; Engineering; Environmental health","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":[0.004204216,0.0002706234,0.0002449842,0.001276167,0.007954059,0.004674627,0.001396487,0.003281331,0.005045034],"category_scores_gemma":[0.005713178,0.0003832954,0.0003684135,0.001728749,0.002828033,0.002875033,0.004713573,0.001836057,0.0004534569],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01042926,"about_ca_system_score_gemma":0.01220416,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04263584,"about_ca_topic_score_gemma":0.06417513,"domain_scores_codex":[0.9947068,0.002871916,0.0002214194,0.0002081451,0.000646187,0.00134564],"domain_scores_gemma":[0.9967117,0.001286644,0.0004251533,0.0002085296,0.0004100262,0.0009579538],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.0001514591,0.001275219,0.1236629,0.0005413276,0.00004997944,0.1039819,0.2789967,0.004884267,0.003444893,0.3724195,0.01708643,0.09350543],"study_design_scores_gemma":[0.00008211993,0.0006413311,0.08634404,0.001434958,0.00006267232,0.04350159,0.5554383,0.02090478,0.00201246,0.03504632,0.254358,0.0001733472],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8499624,0.001194983,0.01412593,0.0455724,0.00009677498,0.0005950398,0.0001158448,0.00007917353,0.08825748],"genre_scores_gemma":[0.9859995,0.0004924668,0.005713737,0.001104915,0.00002866164,0.0001187926,0.00004074783,0.0000112434,0.006489905],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04263584,"threshold_uncertainty_score":0.08477539,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01602520242050214,"score_gpt":0.3584607157855849,"score_spread":0.3424355133650827,"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."}}