{"id":"W2754401678","doi":"10.1016/j.alcr.2017.09.002","title":"Later-life employment trajectories and health","year":2017,"lang":"en","type":"article","venue":"Advances in Life Course Research","topic":"Retirement, Disability, and Employment","field":"Social Sciences","cited_by":34,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto; Public Health Ontario","funders":"European Research Council; Economic and Social Research Council; Medical Research Council; National Institute on Aging; Social Sciences and Humanities Research Council of Canada; Canadian Institutes of Health Research; University of Michigan; U.S. Social Security Administration","keywords":"Life course approach; Demographic economics; Demographics; Logistic regression; Health and Retirement Study; Matching (statistics); Work (physics); Cohort; Psychology; Ordered logit; Longitudinal study; Demography; Gerontology; Labour economics; Medicine; Economics; Sociology; Social psychology","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":[],"consensus_categories":[],"category_scores_codex":[0.0008125149,0.0001229587,0.0001499024,0.0006296413,0.0003880539,0.0006225375,0.0002239013,0.0003838033,0.002590675],"category_scores_gemma":[0.002781734,0.0001134886,0.0003170306,0.0007239031,0.0001545448,0.0005037404,0.0007299007,0.0004123859,0.0004561387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002518962,"about_ca_system_score_gemma":0.0002348842,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01076154,"about_ca_topic_score_gemma":0.01600686,"domain_scores_codex":[0.9998184,0.00004434238,0.00001512188,0.0000335365,0.00001890544,0.00006967114],"domain_scores_gemma":[0.9990577,0.0001534025,0.0003662746,0.00007773237,0.0001097859,0.0002351354],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00008678038,0.00004610661,0.992342,0.00001274201,0.00003622827,0.00005471464,0.0005428437,0.0002481897,0.0001626614,0.0001905971,0.0001421486,0.006134977],"study_design_scores_gemma":[9.616806e-7,0.00003036542,0.9989833,0.00001154649,0.00000473265,0.00003707659,0.0003224738,0.0001801209,0.00002784397,0.000109385,0.0002897498,0.000002298587],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.998262,0.0002795239,0.000169623,0.0001506412,0.000003974378,0.000004109707,0.000536047,0.000002649401,0.0005914672],"genre_scores_gemma":[0.998771,0.0001845123,0.00008845438,0.0000165362,0.000004143404,0.000005120218,0.0005461332,0.000001707983,0.0003823086],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01076154,"threshold_uncertainty_score":0.02139777,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4470407657523408,"score_gpt":0.5970757961885655,"score_spread":0.1500350304362248,"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."}}