{"id":"W4407228607","doi":"10.1177/07334648251316973","title":"Scheduling, Income, and School Closures: Unlocking the Key Drivers of Home Care Personal Support Unplanned Absences Through Time-to-Event Regression Analysis","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Gerontology","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University; University of Waterloo; Public Health Ontario; University of Toronto; University of Ottawa","funders":"","keywords":"Health care; Pandemic; Business; Government (linguistics); Moral hazard; Demographic economics; Actuarial science; Medicine; Incentive; Economics; Coronavirus disease 2019 (COVID-19); Economic growth","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007134944,0.0001831007,0.0008188863,0.0004729131,0.0004812993,0.00001254209,0.0003088525,0.0002612147,0.0005489144],"category_scores_gemma":[0.0000998456,0.0001192824,0.0002153718,0.0006776782,0.0001502598,0.00007031175,0.0001257205,0.0006439668,0.00002552335],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001903738,"about_ca_system_score_gemma":0.0006172719,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001154158,"about_ca_topic_score_gemma":0.0002365692,"domain_scores_codex":[0.9979271,0.0002359583,0.0008952623,0.0002335952,0.0003424672,0.000365643],"domain_scores_gemma":[0.9978561,0.0005292431,0.0009005691,0.000220253,0.0003648971,0.0001288765],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.006704201,0.0002578796,0.6343017,0.001146478,0.005383077,0.0001148923,0.2768897,0.001985264,0.00313229,0.001902908,0.05184329,0.01633835],"study_design_scores_gemma":[0.005889234,0.001342791,0.5669649,0.001392626,0.00447852,0.0000437017,0.3885627,0.0002729848,0.0005114973,0.002826064,0.02708241,0.0006326219],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9888232,0.001624688,0.0006529664,0.002328066,0.0006487819,0.0003225541,0.0000133077,0.00001446285,0.005571923],"genre_scores_gemma":[0.9974902,0.0001707408,0.001335635,0.0002994984,0.0001756026,0.000009647019,0.000008314829,0.000009476312,0.0005008555],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1116731,"threshold_uncertainty_score":0.6010228,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01979455816683817,"score_gpt":0.3622385472086044,"score_spread":0.3424439890417663,"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."}}