{"id":"W2998315221","doi":"10.1186/s12961-019-0502-6","title":"Optimisation: defining and exploring a concept to enhance the impact of public health initiatives","year":2019,"lang":"en","type":"article","venue":"Health Research Policy and Systems","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":65,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; University of British Columbia","funders":"National Center for Advancing Translational Sciences; National Health and Medical Research Council; National Cancer Institute; Hunter Medical Research Institute","keywords":"Public health; Context (archaeology); Thematic analysis; Delphi method; Process management; Health services research; Stakeholder; Process (computing); Health policy; Health administration; Management science; Knowledge management; Public relations; Computer science; Medicine; Qualitative research; Business; Political science; Engineering; Nursing; Sociology","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":["metaresearch","sts"],"consensus_categories":[],"category_scores_codex":[0.03342345,0.0001632657,0.0005563815,0.0006962272,0.002123715,0.00008270499,0.0002855994,0.00007412008,0.0000709633],"category_scores_gemma":[0.007979998,0.0001119113,0.00003397439,0.001621529,0.0002848724,0.0005362903,0.0002850256,0.0007924337,0.0001077183],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001112312,"about_ca_system_score_gemma":0.01183591,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04736339,"about_ca_topic_score_gemma":0.0003661553,"domain_scores_codex":[0.9834142,0.01147123,0.001443399,0.0004576752,0.001021265,0.002192248],"domain_scores_gemma":[0.9866195,0.0100741,0.000601288,0.0004933889,0.0007129131,0.001498778],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.00006122291,0.00004439693,0.07688004,0.004440735,0.00004553204,7.285681e-7,0.7365139,0.0001421122,0.0001252338,0.1331327,0.01747587,0.03113759],"study_design_scores_gemma":[0.002163756,0.004862174,0.5751077,0.003234077,0.000001641235,0.00002421904,0.2990436,0.002283369,0.00002659178,0.0005622375,0.1122324,0.0004582379],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8660446,0.001427935,0.0003539507,0.1238135,0.0002259132,0.004898265,0.0001864195,0.00004223595,0.003007148],"genre_scores_gemma":[0.9893295,0.001313828,0.0001603393,0.007518588,0.000332871,0.0008856753,0.000005468981,0.00002119913,0.0004325278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4982277,"threshold_uncertainty_score":0.9991754,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9371894666115107,"score_gpt":0.7684890779729129,"score_spread":0.1687003886385978,"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."}}