{"id":"W7136098244","doi":"","title":"Cikkismertetés: A szociodemográfiai adatgyűjtést támogató és akadályozó tényezők a kanadai egészségügyben: egy többhelyszínes esettanulmány = Article review: Facilitators and barriersof sociodemographic data collection in Canadian health care settings: a multisite case study evaluation","year":2019,"lang":"hu","type":"article","venue":"Repository of the Academy's Library (Library of the Hungarian Academy of Sciences)","topic":"Health, psychology, and well-being","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Data collection; Health care; Public health; Work (physics); Qualitative research; Agency (philosophy)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005276877,0.0006450302,0.001105278,0.005310419,0.003896654,0.005911595,0.001066607,0.0008483378,0.005000925],"category_scores_gemma":[0.005493024,0.0005094165,0.000688607,0.009046997,0.0020342,0.002555522,0.003396757,0.001358433,0.0003423236],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01829406,"about_ca_system_score_gemma":0.05806701,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6144066,"about_ca_topic_score_gemma":0.6967606,"domain_scores_codex":[0.9971752,0.0004961963,0.0003575707,0.0002705022,0.001036445,0.0006641101],"domain_scores_gemma":[0.9980147,0.0004749334,0.0003456578,0.00006665471,0.00082961,0.0002684697],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0003544275,0.0008556956,0.6490273,0.006136018,0.0004825443,0.00344589,0.1147577,0.0002068989,0.00084175,0.009066623,0.01410757,0.2007176],"study_design_scores_gemma":[0.00007563432,0.0001890062,0.7049162,0.006057478,0.0007812456,0.002046534,0.2268992,0.0001935447,0.0006432725,0.001442833,0.05663739,0.0001176715],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9182683,0.04748011,0.001032513,0.01298072,0.0004189243,0.001011718,0.003761348,0.00001770649,0.01502864],"genre_scores_gemma":[0.9446348,0.04485223,0.001924851,0.001187086,0.00008652588,0.001091451,0.002145303,0.00001564011,0.004062108],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3855934,"threshold_uncertainty_score":0.7757283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03975750827235697,"score_gpt":0.3745662611165625,"score_spread":0.3348087528442055,"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."}}