{"id":"W4252566660","doi":"10.32920/ryerson.14653416","title":"An Ecological Model for Culturally Sensitive Care for Older Immigrants: Best Practices and Lessons Learned from Ethno-Specific Long-Term Care","year":2021,"lang":"en","type":"preprint","venue":"","topic":"Geriatric Care and Nursing Homes","field":"Health Professions","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Long-term care; Immigration; Exploratory research; Context (archaeology); Qualitative research; Stressor; Cultural diversity; Nursing; Multiculturalism; Psychology; Medicine; Public relations; Sociology; Political science; Geography; Pedagogy; Social science","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02469406,0.001126425,0.0005997359,0.002508616,0.01660796,0.01006052,0.003664464,0.001885611,0.00274315],"category_scores_gemma":[0.01039882,0.0007233123,0.000708125,0.001837138,0.03588178,0.006916197,0.01081505,0.003447714,0.000250852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.02620497,"about_ca_system_score_gemma":0.03958718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1158025,"about_ca_topic_score_gemma":0.3075769,"domain_scores_codex":[0.9792939,0.01855535,0.0003034979,0.0005612151,0.0006962006,0.0005897246],"domain_scores_gemma":[0.9919289,0.004812237,0.0003346974,0.0008141429,0.0008617616,0.001248263],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.00002140949,0.0001436231,0.007124386,0.0003928216,0.00001948304,0.0007526028,0.9094433,0.0005605645,0.0002427051,0.05494368,0.001738284,0.02461716],"study_design_scores_gemma":[0.00002124755,0.0001015854,0.005587046,0.001538772,0.00004046114,0.000564845,0.8882241,0.001045855,0.0001851092,0.03418389,0.06845639,0.00005062738],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5698768,0.01321474,0.1468572,0.1071225,0.0009769833,0.002991143,0.0002391677,0.0004051807,0.1583163],"genre_scores_gemma":[0.8886601,0.005167822,0.09410167,0.002996784,0.00004670323,0.001585688,0.00009428915,0.00009991796,0.007246975],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1158025,"threshold_uncertainty_score":0.230257,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2024900905195239,"score_gpt":0.476114244396026,"score_spread":0.273624153876502,"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."}}