{"id":"W2726974339","doi":"10.1136/jech-2017-208979","title":"A glossary of terms for understanding political aspects in the implementation of Health in All Policies (HiAP)","year":2017,"lang":"en","type":"article","venue":"Journal of Epidemiology & Community Health","topic":"Global Public Health Policies and Epidemiology","field":"Business, Management and Accounting","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University; St. Michael's Hospital; University of Toronto","funders":"Canadian Institutes of Health Research","keywords":"Politics; Glossary; Popularity; Public health; Government (linguistics); Medicine; Public relations; Political science; Public administration; Law; Nursing","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":[0.005874446,0.002751245,0.00219223,0.01076422,0.00296106,0.006358043,0.002640033,0.003881977,0.09678846],"category_scores_gemma":[0.03270306,0.0009932976,0.00187666,0.01552828,0.003287297,0.01086673,0.004402303,0.006742658,0.04614877],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004468787,"about_ca_system_score_gemma":0.00428636,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009750343,"about_ca_topic_score_gemma":0.01125391,"domain_scores_codex":[0.9934243,0.002149471,0.002183713,0.0005599931,0.001390836,0.0002917108],"domain_scores_gemma":[0.9780393,0.0145903,0.002504655,0.001526129,0.002818741,0.0005208874],"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":[0.0000539916,0.00003040752,0.0002012098,0.004433442,0.00001905295,0.0001502389,0.002023755,0.0002325857,0.0004264321,0.1121868,0.8324336,0.04780848],"study_design_scores_gemma":[0.000007384427,0.00001138372,0.0003327843,0.002678209,0.000007747274,0.0001443644,0.000338745,0.00006821982,0.00003098269,0.01162613,0.984735,0.00001901274],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.002479439,0.2329554,0.066277,0.06121598,0.03334709,0.005338645,0.1155984,0.002356439,0.4804316],"genre_scores_gemma":[0.03830273,0.3270695,0.1679145,0.08393288,0.02587676,0.02277493,0.1473493,0.006176108,0.1806034],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.09678846,"threshold_uncertainty_score":0.3237897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3667605038230258,"score_gpt":0.5075707925414965,"score_spread":0.1408102887184707,"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."}}