{"id":"W4405065130","doi":"10.2196/60368","title":"Strategies to Implement a Community-Based, Longitudinal Cohort Study: The Whole Communities-Whole Health Case Study","year":2024,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Mental Health; Eli Lilly and Company","keywords":"Multidisciplinary approach; Temporality; Schedule; Data collection; Qualitative property; Cohort; Longitudinal study; Process (computing); Community health; Computer science; Plan (archaeology); Population; Medical education; Knowledge management; Process management; Medicine; Public health; Nursing; Sociology; Geography; Engineering; Environmental health","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.3039599,0.001203594,0.001063244,0.003946318,0.01808714,0.006280917,0.007629512,0.006987412,0.00766496],"category_scores_gemma":[0.159979,0.002322814,0.001715026,0.002958821,0.005180099,0.009939807,0.0199454,0.008349996,0.002305461],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01025338,"about_ca_system_score_gemma":0.07845703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03354116,"about_ca_topic_score_gemma":0.07704721,"domain_scores_codex":[0.8086032,0.1709995,0.00434088,0.002997681,0.00714102,0.00591779],"domain_scores_gemma":[0.8476248,0.06677294,0.008028491,0.02291738,0.02564835,0.02900794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.001448808,0.01634199,0.1260343,0.004511011,0.0003810145,0.008737597,0.2809708,0.002569603,0.00472984,0.0643975,0.08028021,0.4095972],"study_design_scores_gemma":[0.004823281,0.01597596,0.05131247,0.01062842,0.0005463344,0.004526618,0.4526053,0.009460378,0.006938409,0.06686929,0.3754611,0.0008525029],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2193423,0.002917822,0.2786343,0.1977679,0.003842437,0.2568296,0.001775101,0.00122299,0.03766752],"genre_scores_gemma":[0.2251845,0.001566794,0.5168525,0.03145374,0.0004234533,0.217757,0.0005633211,0.0001394576,0.006059155],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3039599,"threshold_uncertainty_score":0.8583413,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7656232995596542,"score_gpt":0.7444449975275405,"score_spread":0.02117830203211379,"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."}}