{"id":"W7162019789","doi":"10.82308/834","title":"Bayesian construct validation leveraging expert knowledge for questionnaire instruments used in primary care research and practice","year":2024,"lang":"en","type":"dissertation","venue":"","topic":"Psychometric Methodologies and Testing","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Construct (python library); Bayesian probability; Inference; Domain (mathematical analysis); Subject-matter expert; Bayesian inference; Process (computing); Bayesian network","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0131016,0.0002644687,0.0005071915,0.002611859,0.0003177971,0.0009425877,0.0005229184,0.0003573118,0.00004159052],"category_scores_gemma":[0.1011462,0.000211344,0.00008215093,0.002966429,0.00009466523,0.0006202081,0.0001443546,0.0006105647,0.00002284715],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003961171,"about_ca_system_score_gemma":0.0006234552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002732704,"about_ca_topic_score_gemma":0.0002474771,"domain_scores_codex":[0.9947008,0.001399635,0.0009674346,0.001162904,0.001349524,0.0004196584],"domain_scores_gemma":[0.9607738,0.03675518,0.0003127141,0.0004523687,0.001596609,0.0001093169],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.0002625329,0.00006386837,0.01598497,0.0006147077,0.00003591831,0.0000101638,0.02098621,0.000003313748,0.0003809784,0.00165827,0.002877602,0.9571215],"study_design_scores_gemma":[0.00397943,0.0009077475,0.1139442,0.007194669,0.0001072692,0.0001350931,0.6385959,0.003913026,0.004158786,0.1843347,0.04085,0.001879208],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7291247,0.02007711,0.01033629,0.0008674453,0.007713153,0.002894484,0.00003588953,0.000227678,0.2287233],"genre_scores_gemma":[0.9364627,0.0003307571,0.05448579,0.00005966294,0.0002412665,0.0002552462,0.0003435063,0.0000536072,0.007767468],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9552423,"threshold_uncertainty_score":0.9089395,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5363131275926621,"score_gpt":0.5747210984084848,"score_spread":0.0384079708158227,"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."}}