{"id":"W2321041115","doi":"10.1332/174426412x620155","title":"Maximising the use of evidence: exploring the intersection between population health intervention research and knowledge translation from a Canadian perspective","year":2012,"lang":"en","type":"article","venue":"Evidence & Policy","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institutes of Health Research; University of Ottawa; Impact; Institute of Population and Public Health; University of Waterloo","funders":"","keywords":"Knowledge translation; Underpinning; Psychological intervention; Intervention (counseling); Perspective (graphical); Evidence-based practice; Population health; Intersection (aeronautics); Public relations; Population; Interrogation; Public health; Political science; Psychology; Sociology; Medicine; Knowledge management; Alternative medicine; Nursing; Engineering; Environmental health; Computer 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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.3575562,0.00191971,0.0040687,0.02152784,0.03476946,0.06179475,0.009725655,0.0113118,0.004012379],"category_scores_gemma":[0.3537375,0.002825966,0.002420131,0.03008796,0.07072993,0.02347706,0.03858697,0.01616678,0.0003674806],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.2691174,"about_ca_system_score_gemma":0.5727266,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.8809504,"about_ca_topic_score_gemma":0.9132475,"domain_scores_codex":[0.5673216,0.3075977,0.02315652,0.008219586,0.06791674,0.02578777],"domain_scores_gemma":[0.4829746,0.4295403,0.01077643,0.01261513,0.05083222,0.01326138],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002204352,0.0001492651,0.005114223,0.008275791,0.0002338942,0.001924555,0.6102038,0.001318032,0.001156733,0.2196236,0.006147439,0.1456323],"study_design_scores_gemma":[0.0002111827,0.0002510654,0.0119828,0.02109038,0.000437998,0.0007867834,0.5428938,0.004261372,0.002230719,0.2114859,0.2038683,0.0004996778],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"empirical","genre_scores_codex":[0.09668113,0.06200889,0.05368529,0.5681789,0.001497746,0.003309385,0.0003817161,0.0002660024,0.2139909],"genre_scores_gemma":[0.8763346,0.02420647,0.0804569,0.01346746,0.0002224746,0.001331257,0.0001073343,0.0001242119,0.003749273],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7308825,"threshold_uncertainty_score":0.8477201,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9786773876900249,"score_gpt":0.756404592869529,"score_spread":0.2222727948204959,"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."}}