{"id":"W4408040979","doi":"10.1017/s1368980025000084","title":"Improving public health data collection approaches across populations: findings from a national evaluation of fruit and vegetable incentives","year":2025,"lang":"en","type":"article","venue":"Public Health Nutrition","topic":"Food Security and Health in Diverse Populations","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Impact","funders":"","keywords":"Incentive; Focus group; Thematic analysis; Data collection; Descriptive statistics; Food security; Public health; Environmental health; Qualitative property; Supplemental Nutrition Assistance Program; Incentive program; Psychology; Qualitative research; Business; Agriculture; Medical education; Medicine; Gerontology; Marketing; Geography; Nursing; Computer science; Food insecurity; Sociology","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3574846,0.0004445194,0.0008168524,0.001112811,0.004357973,0.004223947,0.003479518,0.001853919,0.001645612],"category_scores_gemma":[0.3851011,0.0008798473,0.00111214,0.002315608,0.003035391,0.00436504,0.007617651,0.002569773,0.0002586828],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01702481,"about_ca_system_score_gemma":0.06370625,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03200201,"about_ca_topic_score_gemma":0.03888183,"domain_scores_codex":[0.667635,0.2789226,0.01731312,0.00356359,0.0263065,0.006259247],"domain_scores_gemma":[0.5442157,0.3460788,0.02358869,0.0170389,0.05999858,0.009079308],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.004803845,0.01013629,0.2095797,0.01131566,0.0008167872,0.001056923,0.2008711,0.002795601,0.002358117,0.003053795,0.01275247,0.5404598],"study_design_scores_gemma":[0.00286459,0.02741667,0.5354999,0.01424973,0.00127454,0.00107195,0.2946905,0.007888833,0.009195631,0.003138709,0.1022725,0.0004364722],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9541564,0.0016796,0.006498765,0.01304708,0.0001228284,0.01258299,0.0008277051,0.0001591781,0.01092547],"genre_scores_gemma":[0.9614919,0.001328512,0.02003318,0.003217449,0.00003922587,0.01231419,0.0004711851,0.00006369294,0.001040683],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3574846,"threshold_uncertainty_score":0.7923359,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6613401617211946,"score_gpt":0.5298627295833427,"score_spread":0.1314774321378519,"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."}}