{"id":"W3130509610","doi":"10.1007/s12553-020-00518-2","title":"Guidelines for developing geographically sensitive mobile health applications","year":2021,"lang":"en","type":"article","venue":"Health and Technology","topic":"Mobile Health and mHealth Applications","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Grand Challenges Canada","keywords":"mHealth; Context (archaeology); Mobile technology; Millennium Development Goals; Business; Health care; Digital health; Computer science; Process management; Mobile computing; Developing country; Telecommunications; Economic growth; Geography","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001256804,0.0002452119,0.000715033,0.0004182986,0.00308161,0.000008662163,0.0001594577,0.0004666884,0.00002125106],"category_scores_gemma":[0.0006724884,0.0002468126,0.00006024461,0.001402823,0.0001737797,0.00004894199,0.0001595822,0.0007102297,0.00005170254],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002664915,"about_ca_system_score_gemma":0.009398489,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001878419,"about_ca_topic_score_gemma":0.0008544266,"domain_scores_codex":[0.9957218,0.0002555995,0.001598716,0.0007928924,0.0001587877,0.001472255],"domain_scores_gemma":[0.9953118,0.0005765732,0.000641629,0.0006241268,0.002012014,0.0008338829],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00002567257,0.00007285469,0.003234732,0.002395994,0.00001854156,0.000001342224,0.0001977432,5.16507e-7,0.00004303427,0.4507236,0.02977058,0.5135154],"study_design_scores_gemma":[0.0009187489,0.0003216019,0.0003241351,0.0002332845,0.000005665699,0.0000325199,0.003445069,0.00006701492,0.00002951711,0.01999632,0.9744503,0.0001757964],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"protocol","genre_scores_codex":[0.005834955,0.04897641,0.275334,0.6288339,0.0007121123,0.03742896,0.0004950423,0.001622563,0.0007620965],"genre_scores_gemma":[0.0160249,0.06032959,0.2916586,0.2468754,0.0009545626,0.3814504,0.001133554,0.0001335509,0.001439429],"genre_candidate":"commentary","genre_consensus":null,"teacher_disagreement_score":0.9446797,"threshold_uncertainty_score":0.9999984,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0843946203735879,"score_gpt":0.4898191575081607,"score_spread":0.4054245371345728,"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."}}