{"id":"W2993619594","doi":"10.5539/gjhs.v11n13p158","title":"Experiential Learning Curriculum Delivery Approach for Quality Improvement in Resource Limited Settings: Mobile Learning for Point-of-Care Technologies","year":2019,"lang":"en","type":"article","venue":"Global Journal of Health Science","topic":"Biomedical and Engineering Education","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Experiential learning; Curriculum; Medicine; Health care; Resource (disambiguation); Service delivery framework; Knowledge management; Medical education; Quality (philosophy); Nursing; Service (business); Psychology; Business; Computer science; Pedagogy; Marketing; Political science","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":[],"consensus_categories":[],"category_scores_codex":[0.001354561,0.00009361398,0.0002479014,0.0001589226,0.00008948594,0.00002469915,0.0002802038,0.00006477669,0.000001201095],"category_scores_gemma":[0.0002098369,0.00008167396,0.00006590896,0.0005414468,0.00008221928,0.0001799202,0.00004262701,0.0002329463,2.736219e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000369595,"about_ca_system_score_gemma":0.000155824,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001443259,"about_ca_topic_score_gemma":5.295058e-7,"domain_scores_codex":[0.9986179,0.00001541479,0.0005525498,0.0001565938,0.0002931263,0.0003644444],"domain_scores_gemma":[0.9993749,0.00003945496,0.0002428315,0.00008843078,0.0001829309,0.00007141033],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008108456,0.000194489,0.02869143,0.006199134,0.00002381403,4.214724e-7,0.00347611,0.4622931,0.01407151,0.0002836008,0.0005777034,0.4841076],"study_design_scores_gemma":[0.004646071,0.006956603,0.0194386,0.001850558,0.00002879813,0.00007430982,0.3421357,0.5758998,0.01151593,0.0002130069,0.03624128,0.0009992755],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580458,0.001659162,0.03910656,0.0001283091,0.000490894,0.0004407316,0.000003757783,0.00007581788,0.0000489514],"genre_scores_gemma":[0.9843416,0.00004662498,0.01551524,0.00001756918,0.00003408876,0.00003011415,0.000005466616,0.000006251786,0.000002996998],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4831083,"threshold_uncertainty_score":0.3330567,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009618547040583196,"score_gpt":0.2869723516507987,"score_spread":0.2773538046102155,"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."}}