{"id":"W2523225320","doi":"10.7748/ns.31.4.28.s27","title":"Health visiting cuts only save problems for later","year":2016,"lang":"en","type":"article","venue":"Nursing Standard","topic":"Global Health Workforce Issues","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Canadian Navy","funders":"","keywords":"LOOM; Economic shortage; Psychology; Operations management; Nursing; Medicine; Engineering; Computer science; Artificial intelligence; Government (linguistics)","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":[0.001990583,0.0007287956,0.0007490684,0.0006726256,0.005528507,0.00686539,0.00137331,0.005461724,0.1232161],"category_scores_gemma":[0.011309,0.0004256759,0.001160588,0.0005562349,0.002046601,0.004101432,0.004204276,0.009153589,0.03529283],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002468209,"about_ca_system_score_gemma":0.006781072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01005254,"about_ca_topic_score_gemma":0.02351695,"domain_scores_codex":[0.9969864,0.0007152054,0.00008977793,0.0002405031,0.0006343821,0.001333674],"domain_scores_gemma":[0.9905655,0.0007178021,0.0005806949,0.0007445384,0.00135386,0.006037543],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002378827,0.000515245,0.0108595,0.0002159225,0.00006071834,0.0007867255,0.001795997,0.0001145603,0.00105488,0.01702294,0.8576505,0.1096852],"study_design_scores_gemma":[0.00006816428,0.0004364147,0.02466496,0.0008975766,0.00003984733,0.001377533,0.01157266,0.0001171949,0.0004811121,0.01495111,0.9453301,0.00006325237],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.04560088,0.008049643,0.003923644,0.6026022,0.03810782,0.0001797043,0.00165681,0.002048498,0.2978307],"genre_scores_gemma":[0.2168533,0.004843233,0.005212687,0.1851908,0.01038058,0.0001533114,0.001806232,0.001281353,0.5742785],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1232161,"threshold_uncertainty_score":0.4121989,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04876582334599817,"score_gpt":0.461012493406124,"score_spread":0.4122466700601259,"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."}}