{"id":"W2107711297","doi":"10.1002/asi.22691","title":"Constructing “sense” from evolving health information: A qualitative investigation of information seeking and sense making across sources","year":2012,"lang":"en","type":"article","venue":"Journal of the American Society for Information Science and Technology","topic":"Health Sciences Research and Education","field":"Health Professions","cited_by":79,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta Hospital; University of Alberta","funders":"Social Sciences and Humanities Research Council of Canada; Health Canada; Medical Library Association","keywords":"Information literacy; Credibility; Consistency (knowledge bases); Computer science; Context (archaeology); Information seeking; Construct (python library); Health literacy; Knowledge management; Experiential learning; Psychology; Health care; Epistemology; World Wide Web; Information retrieval; Artificial intelligence; Pedagogy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00901399,0.00008728565,0.0002605134,0.0003669,0.001762099,0.00008785822,0.0002007849,0.00007374318,0.000002172399],"category_scores_gemma":[0.003848502,0.00006162207,0.00005598408,0.002039499,0.002132509,0.01016521,0.0001819822,0.0004208871,0.000002294146],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003616723,"about_ca_system_score_gemma":0.00155364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002614858,"about_ca_topic_score_gemma":0.00001001924,"domain_scores_codex":[0.9975117,0.0001322246,0.001135521,0.00005364204,0.0006505758,0.000516286],"domain_scores_gemma":[0.9932266,0.0007175355,0.003835802,0.0001319551,0.001925703,0.0001624092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00004054327,0.000006301242,0.1243076,0.0003890964,0.00002782087,1.115193e-8,0.7618635,0.000007730935,0.0003820049,0.004490005,0.002993794,0.1054916],"study_design_scores_gemma":[0.0004012729,0.0001470129,0.03685517,0.0002363754,0.000007333626,0.00002801626,0.9510699,0.003001226,0.0002064069,0.00106249,0.006904529,0.00008029159],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9805666,0.00008890803,0.007657742,0.0107467,0.0003649759,0.0004191034,0.00004329367,0.00001762509,0.00009501924],"genre_scores_gemma":[0.9770488,0.00009169427,0.0194387,0.003326394,0.00007260748,0.00001357814,0.000004986443,0.000001891612,0.00000139393],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1892063,"threshold_uncertainty_score":0.9995375,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08423980320096744,"score_gpt":0.4832622485664276,"score_spread":0.3990224453654602,"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."}}