{"id":"W1966978559","doi":"10.1186/1478-4491-7-42","title":"Narrowing the gap between eye care needs and service provision: a model to dynamically regulate the flow of personnel through a multiple entry and exit training programme","year":2009,"lang":"en","type":"article","venue":"Human Resources for Health","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kensington Health","funders":"","keywords":"Computer science; Set (abstract data type); Variety (cybernetics); Stakeholder; Health administration; Workforce; Field (mathematics); Operations research; Service (business); Risk analysis (engineering); Workflow; Health care; Process management; Management science; Business; Economics; Artificial intelligence; Engineering; Marketing","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.001278052,0.00019106,0.0003751016,0.0001104817,0.003851766,0.00005207298,0.0001942333,0.0001610262,0.00000327034],"category_scores_gemma":[0.0001461528,0.0001206443,0.00005122657,0.0003997234,0.0000651388,0.00008727652,0.00006820709,0.000424728,6.287602e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009043993,"about_ca_system_score_gemma":0.0002925313,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006528821,"about_ca_topic_score_gemma":0.001338636,"domain_scores_codex":[0.9977239,0.0004105026,0.0006801786,0.0003223757,0.0002368595,0.0006262147],"domain_scores_gemma":[0.9985445,0.0003496097,0.00024828,0.0003424423,0.0002845043,0.0002306873],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001045766,0.00002775891,0.00255527,0.001075709,0.00001824756,2.126824e-7,0.963719,0.00951196,0.00007756288,0.001478653,0.0001799587,0.02125117],"study_design_scores_gemma":[0.002044266,0.001059596,0.01424879,0.001347074,0.00004797452,0.000002085133,0.4032238,0.5710379,0.00000242093,0.001116887,0.005570761,0.0002984119],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.923384,0.0005084095,0.01012614,0.06173209,0.00003379194,0.003947764,0.00007946961,0.00005982766,0.0001284677],"genre_scores_gemma":[0.9616949,0.00005029153,0.02992232,0.007675486,0.0002063137,0.0002322625,0.0000651129,0.00003163277,0.0001217024],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.561526,"threshold_uncertainty_score":0.9974451,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1516631003608385,"score_gpt":0.4237560730528479,"score_spread":0.2720929726920094,"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."}}