{"id":"W3092015663","doi":"10.1186/s40545-020-00268-6","title":"How do I keep myself safe? Patient perspectives on including reason for use information on prescriptions and medication labels: a qualitative thematic analysis","year":2020,"lang":"en","type":"article","venue":"Journal of Pharmaceutical Policy and Practice","topic":"Pharmaceutical Practices and Patient Outcomes","field":"Medicine","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Research Institute for Aging; Emergent BioSolutions (Canada); Regional Municipality of Waterloo; McMaster University; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Medical prescription; Thematic analysis; Qualitative research; Pharmacy; Qualitative analysis; Medicine; Data science; Medical education; Psychology; Computer science; Family medicine; Nursing; Sociology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001361684,0.0001902692,0.000443582,0.0003761374,0.0001886358,0.000227293,0.00006717327,0.00008813545,0.00002632896],"category_scores_gemma":[0.03683093,0.0001379617,0.0001623216,0.0005087054,0.0001199694,0.002527759,0.00004226411,0.0006561534,0.000004090575],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001178588,"about_ca_system_score_gemma":0.00009603301,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009010866,"about_ca_topic_score_gemma":2.707284e-7,"domain_scores_codex":[0.9977278,0.0007136494,0.0005678512,0.0001844616,0.0005903899,0.0002158728],"domain_scores_gemma":[0.9877747,0.01018831,0.0008581126,0.0001109827,0.0004818067,0.0005861401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.05746877,0.003078117,0.0008841481,0.001867146,0.01049181,0.00005885198,0.6426961,0.0002401466,0.002631927,0.12134,0.004441331,0.1548017],"study_design_scores_gemma":[0.01645089,0.01371431,0.001758163,0.0007809319,0.02224427,0.0005798967,0.3386144,0.07011978,0.002257228,0.003045294,0.5295734,0.0008614569],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.4514938,0.002760492,0.01733224,0.5216628,0.0002234583,0.001929405,0.0001401096,0.00004560535,0.004412099],"genre_scores_gemma":[0.9752856,0.002547148,0.004974732,0.01683844,0.0002955071,0.00001375007,0.000009667417,0.00001144214,0.00002364315],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5251321,"threshold_uncertainty_score":0.9712822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3356135904964345,"score_gpt":0.5209174753615865,"score_spread":0.185303884865152,"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."}}