{"id":"W4414180050","doi":"10.1177/00420980251361626","title":"Profiling caregivers: Caregiving workload, mobility, stress, and remote work difficulties","year":2025,"lang":"en","type":"article","venue":"Urban Studies","topic":"Employment and Welfare Studies","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Fondo Nacional de Desarrollo Científico y Tecnológico; Centro de Desarrollo Urbano Sustentable","keywords":"Profiling (computer programming); Socioeconomic status; Variety (cybernetics); Work (physics); Multivariate analysis; Multivariate statistics; Survey data collection; Focus group","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0003073494,0.0003005981,0.0005767914,0.0001285471,0.002074006,0.00002036049,0.0001512968,0.0001270779,0.00001277156],"category_scores_gemma":[0.000526213,0.0002360851,0.00007903232,0.000384893,0.0003879236,0.0001052383,0.0009030646,0.0004287149,0.00001510721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002009349,"about_ca_system_score_gemma":0.00006319812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003210916,"about_ca_topic_score_gemma":0.0008845293,"domain_scores_codex":[0.9980577,0.0002290029,0.0004504211,0.0004828622,0.0001988357,0.0005812389],"domain_scores_gemma":[0.9982933,0.0008465369,0.0001449883,0.0003247724,0.0003339919,0.00005645257],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005137703,0.00002547966,0.9213263,0.001052831,0.0004824027,0.000005263267,0.02722727,0.00000233909,0.000007553083,0.0009621397,0.04322981,0.005627262],"study_design_scores_gemma":[0.001653631,0.00008242469,0.5702292,0.01045311,0.0003494292,2.146154e-7,0.391769,0.00000815122,0.00009341701,0.002189812,0.02253729,0.0006343328],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8362454,0.1433937,0.00006256723,0.005005272,0.001697464,0.001257087,0.000023866,0.0003957578,0.01191885],"genre_scores_gemma":[0.9641752,0.006296725,0.0003856447,0.0003137469,0.000359917,0.0001384605,0.000005913487,0.0000240427,0.02830033],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3645417,"threshold_uncertainty_score":0.9992251,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05163220019836862,"score_gpt":0.3836458836293186,"score_spread":0.33201368343095,"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."}}