{"id":"W4295249566","doi":"10.12688/hrbopenres.13606.1","title":"Generating actionable insights from free-text care experience survey data using qualitative and computational text analysis: A study protocol","year":2022,"lang":"en","type":"preprint","venue":"HRB Open Research","topic":"Patient Satisfaction in Healthcare","field":"Health Professions","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Health Research Board","keywords":"Knowledge management; Social network analysis; Data science; Health care; Qualitative property; Analytics; Computer science; Medicine; Social media; World Wide Web; Political science","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":["metaepi_narrow","sts","open_science","research_integrity","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.008249389,0.0004240609,0.001037111,0.001001326,0.007096321,0.0004583702,0.003539547,0.0003799618,0.00421029],"category_scores_gemma":[0.003531408,0.0004208066,0.00006150745,0.002122936,0.0002319292,0.000827722,0.03431416,0.00502556,0.00004493439],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001917159,"about_ca_system_score_gemma":0.005727561,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4413703,"about_ca_topic_score_gemma":0.09183311,"domain_scores_codex":[0.951057,0.04111748,0.001642809,0.002462884,0.002893012,0.0008268732],"domain_scores_gemma":[0.9837163,0.008716579,0.0009557655,0.003278024,0.002954492,0.0003788545],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004282426,0.0001651768,0.6537223,0.0005337097,0.0005873014,0.00001559288,0.3249292,0.01767471,0.00001041629,0.00005805264,0.001582214,0.0002930679],"study_design_scores_gemma":[0.001612176,0.0001689261,0.3894456,0.0002893861,0.00006853462,2.523415e-7,0.5427918,0.06345566,0.000001377237,0.0008236773,0.0009427838,0.0003997974],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7786685,0.0001735817,0.0007630686,0.0001169924,0.0004419793,0.2119802,0.006966738,0.00005656128,0.0008323233],"genre_scores_gemma":[0.6419604,0.000005942115,0.01020024,0.00009121237,0.0002150746,0.3384464,0.008612191,0.00007231908,0.0003961972],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3495372,"threshold_uncertainty_score":0.999909,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.781264142193734,"score_gpt":0.702889349699899,"score_spread":0.07837479249383494,"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."}}