{"id":"W2116812043","doi":"10.1186/1475-9276-10-56","title":"Cross-border movement and women's health: how to capture the data","year":2011,"lang":"en","type":"article","venue":"International Journal for Equity in Health","topic":"Migration, Health and Trauma","field":"Psychology","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"St. John’s Health Sciences Centre; McGill University Health Centre; McGill University","funders":"Canadian Institutes of Health Research; McGill University Health Centre; McGill University","keywords":"Ethnic group; Data collection; Immigration; Destinations; Health services research; Survey data collection; Psychology; Social psychology; Sociology; Public health; Geography; Demographic economics; Medicine; Social science; Nursing; Statistics; Economics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1971627,0.001678065,0.002875574,0.009632601,0.004267648,0.0113608,0.005326558,0.004946531,0.009654962],"category_scores_gemma":[0.4461651,0.002611414,0.003283957,0.01123849,0.005948793,0.01515946,0.0108515,0.005983219,0.005032253],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005241682,"about_ca_system_score_gemma":0.02290794,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02672694,"about_ca_topic_score_gemma":0.02092667,"domain_scores_codex":[0.8168675,0.1533415,0.01462367,0.003934619,0.009207061,0.0020255],"domain_scores_gemma":[0.50629,0.3621571,0.02053497,0.04975345,0.05427454,0.006989967],"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":[0.0006822798,0.0006657932,0.0930401,0.02368354,0.0007604706,0.001309982,0.07239479,0.00324062,0.003525265,0.04020571,0.1818893,0.5786021],"study_design_scores_gemma":[0.0004758879,0.0009066137,0.09344795,0.06697602,0.0007833654,0.001696548,0.1295257,0.01206304,0.003927533,0.1083681,0.5810089,0.0008202658],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05690804,0.03027077,0.4939786,0.2809994,0.00918433,0.05424399,0.03543789,0.003277679,0.03569932],"genre_scores_gemma":[0.1163365,0.01111029,0.7852701,0.0154967,0.001149468,0.05998585,0.007457009,0.0005415986,0.002652502],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.1971627,"threshold_uncertainty_score":0.9900414,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1906744936097859,"score_gpt":0.5488470609718331,"score_spread":0.3581725673620473,"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."}}