{"id":"W4200554812","doi":"10.1093/geroni/igab046.2182","title":"Health and Working Beyond Retirement Age: Exploring Racial and Gender Intersectionality","year":2021,"lang":"en","type":"article","venue":"Innovation in Aging","topic":"Optimism, Hope, and Well-being","field":"Psychology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Overtime; Intersectionality; Gerontology; Health and Retirement Study; Ethnic group; Logistic regression; Demography; White (mutation); Psychology; Health equity; Medicine; Public health; Political science; Sociology; Gender studies; Nursing","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003736098,0.0002298597,0.0004525638,0.001479093,0.001400615,0.001227203,0.0007195991,0.0004498857,0.002542217],"category_scores_gemma":[0.007320106,0.0001710878,0.0007520897,0.001272597,0.0007270475,0.001429736,0.002516976,0.0006664558,0.0001291027],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004697756,"about_ca_system_score_gemma":0.0007934591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01436125,"about_ca_topic_score_gemma":0.02464927,"domain_scores_codex":[0.9980945,0.000904428,0.0001038014,0.0003001326,0.0002243264,0.0003729412],"domain_scores_gemma":[0.9966726,0.0008913438,0.001368248,0.0002672849,0.0003586389,0.0004418133],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00006572092,0.00004364651,0.9920282,0.00002264414,0.00009518075,0.000026682,0.00263084,0.00002391088,0.00009516758,0.0004480587,0.0001899762,0.004330197],"study_design_scores_gemma":[0.000002167969,0.00005035704,0.9926474,0.00006613515,0.00004380715,0.00004180542,0.005821204,0.0002161462,0.00005070884,0.0004391769,0.000615716,0.000005428813],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9971702,0.0008134474,0.0002801453,0.0003799294,0.00002223158,0.000009414064,0.0001736217,0.0000021061,0.001148965],"genre_scores_gemma":[0.9994441,0.0001275405,0.000143346,0.00007133792,0.00001746003,0.0000141758,0.00007608787,0.000001259445,0.0001046342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01436125,"threshold_uncertainty_score":0.02855533,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1374586686043004,"score_gpt":0.3741396243949195,"score_spread":0.2366809557906192,"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."}}